Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

[1]  Jorge S. Marques,et al.  Explainable skin lesion diagnosis using taxonomies , 2021, Pattern Recognit..

[2]  Maarten Hoogerwerf,et al.  An open source machine learning framework for efficient and transparent systematic reviews , 2021, Nature Machine Intelligence.

[3]  Jinjun Xiong,et al.  On Interpretability of Artificial Neural Networks: A Survey , 2020, IEEE Transactions on Radiation and Plasma Medical Sciences.

[4]  Kun Kuang,et al.  IB-M: A Flexible Framework to Align an Interpretable Model and a Black-box Model , 2020, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[5]  R. Jeraj,et al.  Interpretation and Visualization Techniques for Deep Learning Models in Medical Imaging , 2020, Physics in medicine and biology.

[6]  Markus H. A. Janse,et al.  Volumetric breast density estimation on MRI using explainable deep learning regression , 2020, Scientific Reports.

[7]  Jaime S. Cardoso,et al.  Interpretability-Guided Content-Based Medical Image Retrieval , 2020, MICCAI.

[8]  Jianwei Niu,et al.  Automatic Medical Image Report Generation with Multi-view and Multi-modal Attention Mechanism , 2020, ICA3PP.

[9]  Joseph D. Janizek,et al.  AI for radiographic COVID-19 detection selects shortcuts over signal , 2020, Nature Machine Intelligence.

[10]  Andrew Y. Ng,et al.  CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV. , 2020, NPJ digital medicine.

[11]  Li Wen,et al.  Generating diagnostic report for medical image by high-middle-level visual information incorporation on double deep learning models , 2020, Comput. Methods Programs Biomed..

[12]  E. Meijering.  A bird’s-eye view of deep learning in bioimage analysis , 2020, Computational and structural biotechnology journal.

[13]  N. Arun,et al.  Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging , 2020, medRxiv.

[14]  Qier Meng,et al.  How to Extract More Information With Less Burden: Fundus Image Classification and Retinal Disease Localization With Ophthalmologist Intervention , 2020, IEEE Journal of Biomedical and Health Informatics.

[15]  Katja Bühler,et al.  Domain aware medical image classifier interpretation by counterfactual impact analysis , 2020, MICCAI.

[16]  Alice C. Yu,et al.  Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax , 2020, Emergency Radiology.

[17]  Rafael Molina,et al.  Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection , 2020, Comput. Methods Programs Biomed..

[18]  Robert J. Gillies,et al.  Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future , 2020, Comput. Biol. Medicine.

[19]  Rui Xu,et al.  Pulmonary Textures Classification via a Multi-Scale Attention Network , 2020, IEEE Journal of Biomedical and Health Informatics.

[20]  Lin Yang,et al.  Interactive thyroid whole slide image diagnostic system using deep representation , 2020, Comput. Methods Programs Biomed..

[21]  Antonella Santone,et al.  Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays , 2020, Computer Methods and Programs in Biomedicine.

[22]  Shaikh Anowarul Fattah,et al.  CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization , 2020, Computers in Biology and Medicine.

[23]  Graziani M,et al.  Concept attribution: Explaining CNN decisions to physicians. , 2020, Computers in biology and medicine.

[24]  Mohammed Benjelloun,et al.  Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images , 2020, International Journal of Computer Assisted Radiology and Surgery.

[25]  Claire Tang.  Discovering Unknown Diseases with Explainable Automated Medical Imaging , 2020, MIUA.

[26]  A. Muacevic,et al.  Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices , 2020, Neuroradiology.

[27]  Hongmin Cai,et al.  Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks , 2020, International Journal of Computer Assisted Radiology and Surgery.

[28]  Youngbin Shin,et al.  COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation , 2020, Journal of medical Internet research.

[29]  S. Aich,et al.  Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network , 2020, Diagnostics.

[30]  Lubaina Ehsan,et al.  HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach , 2020, Inf..

[31]  Ming Qu,et al.  Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks , 2020, Medical Image Anal..

[32]  Carlos A. Silva,et al.  On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. , 2020, Radiology. Artificial intelligence.

[33]  M. Viergever,et al.  Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression. , 2020, Radiology.

[34]  Hao Chen,et al.  Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning , 2020, Medical Image Anal..

[35]  Lin Li,et al.  A deep metric learning approach for histopathological image retrieval. , 2020, Methods.

[36]  Zhiyong Lu,et al.  Automated abnormality classification of chest radiographs using deep convolutional neural networks. , 2020, NPJ digital medicine.

[37]  Russell C. Hardie,et al.  Hybrid machine learning architecture for automated detection and grading of retinal images for diabetic retinopathy , 2020, Journal of medical imaging.

[38]  Johan Wikström,et al.  Identifying Morphological Indicators of Aging With Neural Networks on Large-Scale Whole-Body MRI , 2020, IEEE Transactions on Medical Imaging.

[39]  Beiji Zou,et al.  Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis , 2020, IEEE Journal of Biomedical and Health Informatics.

[40]  Heechan Yang,et al.  Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images , 2020, IEEE Transactions on Medical Imaging.

[41]  Michael Wand,et al.  Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides. , 2020, European urology.

[42]  A. Ben Hamza,et al.  Melanoma detection using adversarial training and deep transfer learning , 2020, Physics in medicine and biology.

[43]  Richard D. White,et al.  Using Transfer Learning and Class Activation Maps Supporting Detection and Localization of Femoral Fractures on Anteroposterior Radiographs , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[44]  Kyunghyun Cho,et al.  Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[45]  Gang Li,et al.  Multi-Branch Deformable Convolutional Neural Network with Label Distribution Learning for Fetal Brain Age Prediction , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[46]  Tanveer Syeda-Mahmood,et al.  Looking in the Right Place for Anomalies: Explainable Ai Through Automatic Location Learning , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[47]  Liansheng Wang,et al.  Weakly-Supervised Balanced Attention Network for Gastric Pathology Image Localization and Classification , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[48]  Chengfei Cai,et al.  Prior-Aware CNN with Multi-Task Learning for Colon Images Analysis , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[49]  Wei-Shi Zheng,et al.  Fusing Metadata and Dermoscopy Images for Skin Disease Diagnosis , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[50]  Manish Gupta,et al.  Region of Interest Identification for Cervical Cancer Images , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[51]  Biplab Banerjee,et al.  Compact Representation Learning Using Class Specific Convolution Coders - Application to Medical Image Classification , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[52]  Huazhu Fu,et al.  SUNet: A Lesion Regularized Model for Simultaneous Diabetic Retinopathy and Diabetic Macular Edema Grading , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[53]  Guotong Xie,et al.  OCT Image Quality Evaluation Based on Deep and Shallow Features Fusion Network , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[54]  Libertario Demi,et al.  Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results , 2020, IEEE Journal of Biomedical and Health Informatics.

[55]  Mohammad Saniee Abadeh,et al.  Brain MRI analysis using a deep learning based evolutionary approach , 2020, Neural Networks.

[56]  Masahiro Oda,et al.  Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis , 2020, Medical Imaging.

[57]  Lubomir M. Hadjiiski,et al.  Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI , 2020, Medical Imaging.

[58]  Takayuki Okatani,et al.  A hyperacute stroke segmentation method using 3D U-Net integrated with physicians’ knowledge for NCCT , 2020, Medical Imaging.

[59]  Mannudeep K. Kalra,et al.  Multi-task learning for mortality prediction in LDCT images , 2020, Medical Imaging.

[60]  Janne J. Näppi,et al.  Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis , 2020, Medical Imaging.

[61]  Won Hwa Kim,et al.  Weakly-supervised US breast tumor characterization and localization with a box convolution network , 2020, Medical Imaging.

[62]  Sébastien Ourselin,et al.  Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology , 2020, International Journal of Computer Assisted Radiology and Surgery.

[63]  Yan Qiang,et al.  Multi-branch cross attention model for prediction of KRAS mutation in rectal cancer with t2-weighted MRI , 2020, Applied Intelligence.

[64]  Curtis P. Langlotz,et al.  AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining , 2020, Scientific Reports.

[65]  Ryanne A. Brown,et al.  Impact of a deep learning assistant on the histopathologic classification of liver cancer. , 2020, NPJ digital medicine.

[66]  Michael Scholz,et al.  Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations , 2020, Epilepsia.

[67]  E. J. Ha,et al.  Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training , 2020, European Radiology.

[68]  D. Felson,et al.  Assessment of knee pain from MR imaging using a convolutional Siamese network , 2020, European Radiology.

[69]  A. Kori,et al.  Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis , 2020, Frontiers in Computational Neuroscience.

[70]  Joon Woo Lee,et al.  Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph , 2020, European Radiology.

[71]  Zhang Yi,et al.  Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks , 2020, Medical Image Anal..

[72]  J. H. Sohn,et al.  Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs. , 2020, Radiology.

[73]  Xiaofei Wang,et al.  A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection , 2020, IEEE Transactions on Medical Imaging.

[74]  M. J. Carreira,et al.  Deep Neural Networks for Chronological Age Estimation From OPG Images , 2020, IEEE Transactions on Medical Imaging.

[75]  Jing Qin,et al.  Domain-invariant interpretable fundus image quality assessment , 2020, Medical Image Anal..

[76]  Felix K. Kopp,et al.  Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography , 2020, Biomedical physics & engineering express.

[77]  Axel Saalbach,et al.  Localization of Critical Findings in Chest X-Ray Without Local Annotations Using Multi-Instance Learning , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[78]  Lingyun Jiang,et al.  Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism , 2020, Complex..

[79]  Jaime S. Cardoso,et al.  Offline computer-aided diagnosis for Glaucoma detection using fundus images targeted at mobile devices , 2020, Comput. Methods Programs Biomed..

[80]  Jong Chul Ye,et al.  Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis , 2020, Frontiers in Neuroscience.

[81]  Daniel C. Castro,et al.  Causality matters in medical imaging , 2019, Nature Communications.

[82]  Shenghua Gao,et al.  Sparse-Gan: Sparsity-Constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image , 2019, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[83]  Richard D. White,et al.  Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network , 2019, Comput. Medical Imaging Graph..

[84]  Richard D. White,et al.  Coronary Artery Classification and Weakly Supervised Abnormality Localization on Coronary CT Angiography with 3-Dimensional Convolutional Neural Networks , 2019, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[85]  David Zhang,et al.  Lesion Location Attention Guided Network for Multi-Label Thoracic Disease Classification in Chest X-Rays , 2019, IEEE Journal of Biomedical and Health Informatics.

[86]  A. Campilho,et al.  DR|GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images , 2019, Medical Image Anal..

[87]  D. Torigian,et al.  Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses , 2019, MICCAI.

[88]  Klaus-Robert Müller,et al.  Resolving challenges in deep learning-based analyses of histopathological images using explanation methods , 2019, Scientific Reports.

[89]  Mathieu Lamard,et al.  Automatic detection of multiple pathologies in fundus photographs using spin-off learning , 2019, Medical image analysis.

[90]  Konstantinos Kamnitsas,et al.  Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling , 2019, ArXiv.

[91]  Marleen de Bruijne,et al.  Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks , 2019, Medical Image Anal..

[92]  Tao Xu,et al.  Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms , 2019, IEEE Journal of Biomedical and Health Informatics.

[93]  Chunhua Shen,et al.  A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification , 2019, IEEE Transactions on Medical Imaging.

[94]  M. Kalra,et al.  Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping , 2018, Medical Image Anal..

[95]  Il-Seok Oh,et al.  Regional Multi-Scale Approach for Visually Pleasing Explanations of Deep Neural Networks , 2018, IEEE Access.

[96]  Michael C. Kampffmeyer,et al.  Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps , 2018, Medical Image Anal..

[97]  Xuming Zhang,et al.  Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet , 2020, IEEE Access.

[98]  K. Masamune,et al.  Prediction of lower-grade glioma molecular subtypes using deep learning , 2019, Journal of Neuro-Oncology.

[99]  T. Leiner,et al.  Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning. , 2019, NPJ digital medicine.

[100]  Rafik Goubran,et al.  An integrated approach for medical abnormality detection using deep patch convolutional neural networks , 2019, The Visual Computer.

[101]  D. Lynch,et al.  Deep Learning Enables Automatic Classification of Emphysema Pattern at CT. , 2019, Radiology.

[102]  Junyi Ji,et al.  Gradient-based Interpretation on Convolutional Neural Network for Classification of Pathological Images , 2019, 2019 International Conference on Information Technology and Computer Application (ITCA).

[103]  Sarvnaz Karimi,et al.  From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach , 2019, 2019 Digital Image Computing: Techniques and Applications (DICTA).

[104]  Kary Främling,et al.  Explaining Machine Learning-Based Classifications of In-Vivo Gastral Images , 2019, 2019 Digital Image Computing: Techniques and Applications (DICTA).

[105]  G. Birk,et al.  Deep learning enables pathologist-like scoring of NASH models , 2019, Scientific Reports.

[106]  Dong Ni,et al.  Multi-task learning for quality assessment of fetal head ultrasound images , 2019, Medical Image Anal..

[107]  Stanley Durrleman,et al.  Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis , 2019, Medical Image Anal..

[108]  Yoshimitsu Aoki,et al.  Weakly Supervised Domain Adaptation with Point Supervision in Histopathological Image Segmentation , 2019, ACPR Workshops.

[109]  Masahiro Murakawa,et al.  Prototype-Based Interpretation of Pathological Image Analysis by Convolutional Neural Networks , 2019, ACPR.

[110]  For the Alzheimer’s Disease Neuroimaging Initiative,et al.  Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer’s disease to Parkinson’s disease , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[111]  X. Cui,et al.  Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. , 2019, Radiology.

[112]  Sheng Huang,et al.  KGZNet:Knowledge-Guided Deep Zoom Neural Networks for Thoracic Disease Classification , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[113]  Zhangxing Bian,et al.  Weakly Supervised Vitiligo Segmentation in Skin Image through Saliency Propagation , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[114]  Yoko Kato,et al.  Automated Stenosis Detection and Classification in X-ray Angiography Using Deep Neural Network , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[115]  Mei Yu,et al.  An Attention-based Semi-supervised Neural Network for Thyroid Nodules Segmentation , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[116]  Dmitry V. Dylov,et al.  Multitask and Multimodal Neural Network Model for Interpretable Analysis of X-ray Images , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[117]  Qinghua Zheng,et al.  Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network , 2019, 2019 IEEE International Conference on Data Mining (ICDM).

[118]  Zhengwu Zhang,et al.  Mapping Brain Structural Connectivities to Functional Networks Via Graph Encoder-Decoder With Interpretable Latent Embeddings , 2019, 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[119]  Blaz Meden,et al.  Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review , 2019, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

[120]  Aeilko H. Zwinderman,et al.  Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke , 2019, Comput. Biol. Medicine.

[121]  Zhongchao Shi,et al.  Towards Automatic Diagnosis from Multi-modal Medical Data , 2019, iMIMIC/ML-CDS@MICCAI.

[122]  Masahiro Ogino,et al.  Guideline-Based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules , 2019, iMIMIC/ML-CDS@MICCAI.

[123]  Marc Modat,et al.  Investigating Image Registration Impact on Preterm Birth Classification: An Interpretable Deep Learning Approach , 2019, SUSI/PIPPI@MICCAI.

[124]  Chandan Singh,et al.  Definitions, methods, and applications in interpretable machine learning , 2019, Proceedings of the National Academy of Sciences.

[125]  Yiqiang Zhan,et al.  Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Prediction of MCI Progression , 2019, MICCAI.

[126]  Chunfeng Lian,et al.  End-to-End Dementia Status Prediction from Brain MRI Using Multi-task Weakly-Supervised Attention Network , 2019, MICCAI.

[127]  Xiaofei Wang,et al.  Pathology-Aware Deep Network Visualization and Its Application in Glaucoma Image Synthesis , 2019, MICCAI.

[128]  Daniel L. Rubin,et al.  Doubly Weak Supervision of Deep Learning Models for Head CT , 2019, MICCAI.

[129]  Wei-Shi Zheng,et al.  Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network , 2019, MICCAI.

[130]  Automated Enriched Medical Concept Generation for Chest X-ray Images , 2019, iMIMIC/ML-CDS@MICCAI.

[131]  Konstantinos N. Plataniotis,et al.  HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[132]  Robert M. Nishikawa,et al.  Detecting mammographically occult cancer in women with dense breasts using deep convolutional neural network and Radon Cumulative Distribution Transform , 2019, Journal of medical imaging.

[133]  Santanu Chaudhury,et al.  Novel relative relevance score for estimating brain connectivity from fMRI data using an explainable neural network approach , 2019, Journal of Neuroscience Methods.

[134]  Jitender Saini,et al.  HR-CAM: Precise Localization of Pathology Using Multi-level Learning in CNNs , 2019, MICCAI.

[135]  M. He,et al.  A self-adaptive deep learning method for automated eye laterality detection based on color fundus photography , 2019, PloS one.

[136]  Kerstin Ritter,et al.  Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification , 2019, iMIMIC/ML-CDS@MICCAI.

[137]  Yuxiang Xing,et al.  Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization , 2019, Comput. Math. Methods Medicine.

[138]  Albert C. S. Chung,et al.  CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning , 2019, MICCAI.

[139]  Mário J. Silva,et al.  A Multi-modal Deep Learning Method for Classifying Chest Radiology Exams , 2019, EPIA.

[140]  Won-Chul Bang,et al.  Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network , 2019, European Radiology.

[141]  May D Wang,et al.  Learning to Evaluate Color Similarity for Histopathology Images using Triplet Networks , 2019, BCB.

[142]  Yen-Wei Chen,et al.  A Dual-Attention Dilated Residual Network for Liver Lesion Classification and Localization on CT Images , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[143]  Sally Shrapnel,et al.  Deep neural network or dermatologist? , 2019, iMIMIC/ML-CDS@MICCAI.

[144]  Weipeng Wang,et al.  Study on Medical Image Report Generation Based on Improved Encoding-Decoding Method , 2019, ICIC.

[145]  Martin Weygandt,et al.  Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification , 2019, Front. Aging Neurosci..

[146]  Wesley De Neve,et al.  Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning † , 2019, Applied Sciences.

[147]  J. Alison Noble,et al.  Incremental Learning of Fetal Heart Anatomies Using Interpretable Saliency Maps , 2019, MIUA.

[148]  Jiebo Luo,et al.  Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment , 2019, MICCAI.

[149]  Marie-Francine Moens,et al.  Justifying Diagnosis Decisions by Deep Neural Networks , 2019, J. Biomed. Informatics.

[150]  Eric Z. Chen,et al.  From Deep Learning Towards Finding Skin Lesion Biomarkers , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[151]  Christoph M. Friedrich,et al.  Variations on Branding with Text Occurrence for Optimized Body Parts Classification , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[152]  Paul Sajda,et al.  Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[153]  Kang Yang,et al.  An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[154]  Kai Gao,et al.  Dense-CAM: Visualize the Gender of Brains with MRI Images , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[155]  Jiao Li,et al.  A Computational Framework Towards Medical Image Explanation , 2019, KR4HC/ProHealth/TEAAM@AIME.

[156]  Lin Wu,et al.  CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels , 2019, MICCAI.

[157]  Roie Melamed,et al.  Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms. , 2019, Radiology.

[158]  Fabio A. González,et al.  Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography , 2019, Comput. Methods Programs Biomed..

[159]  Ilkay Öksüz,et al.  Global and Local Interpretability for Cardiac MRI Classification , 2019, MICCAI.

[160]  Gregory D. Hager,et al.  Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks , 2019, Journal of Digital Imaging.

[161]  Yong Man Ro,et al.  Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis , 2019, iMIMIC/ML-CDS@MICCAI.

[162]  Hao Chen,et al.  Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis , 2019, MICCAI.

[163]  Nassir Navab,et al.  Multi-task learning of a deep k-nearest neighbour network for histopathological image classification and retrieval , 2019, bioRxiv.

[164]  Ghassan Hamarneh,et al.  Melanoma Recognition via Visual Attention , 2019, IPMI.

[165]  V. KranthiKiranG.,et al.  Automatic Classification of Whole Slide Pap Smear Images Using CNN With PCA Based Feature Interpretation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[166]  J. Duncan,et al.  Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features , 2019, European Radiology.

[167]  Nassir Navab,et al.  Learning Interpretable Features via Adversarially Robust Optimization , 2019, MICCAI.

[168]  Dongmei Fu,et al.  Diagnose Chest Pathology in X-ray Images by Learning Multi-Attention Convolutional Neural Network , 2019, 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC).

[169]  Pedro Costa,et al.  EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks for Diabetic Retinopathy Detection , 2019, 2019 16th International Conference on Machine Vision Applications (MVA).

[170]  K. Cao,et al.  Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network , 2019, European Radiology.

[171]  Gregory Hager,et al.  Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning , 2019, Pediatric Radiology.

[172]  Michael Scheel,et al.  Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation , 2019, NeuroImage: Clinical.

[173]  Nassir Navab,et al.  Learning Interpretable Disentangled Representations using Adversarial VAEs , 2019, DART/MIL3ID@MICCAI.

[174]  Ronald M. Summers,et al.  Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[175]  Harshit Pande,et al.  Deep Learning for Weak Supervision of Diabetic Retinopathy Abnormalities , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[176]  Sameer Antani,et al.  Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities , 2019, Diagnostics.

[177]  Sonal Gore,et al.  Predictive and discriminative localization of IDH genotype in high grade gliomas using deep convolutional neural nets , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[178]  Chien-Hung Liao,et al.  Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs , 2019, European Radiology.

[179]  Eric P. Xing,et al.  Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation , 2019, AAAI.

[180]  Heinz Handels,et al.  Interpretable explanations of black box classifiers applied on medical images by meaningful perturbations using variational autoencoders , 2019, Medical Imaging: Image Processing.

[181]  Joseph Y. Lo,et al.  Classification of chest CT using case-level weak supervision , 2019, Medical Imaging.

[182]  George R. Thoma,et al.  Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs , 2019, Medical Imaging.

[183]  Yong Man Ro,et al.  Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis , 2019, Medical Imaging.

[184]  Jitender Saini,et al.  Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI , 2019, NeuroImage: Clinical.

[185]  Ming Dong,et al.  Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery Using Diffusion Tractography and Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[186]  Nikolas Lessmann,et al.  Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT , 2019, IEEE Transactions on Medical Imaging.

[187]  Vineeth N. Balasubramanian,et al.  Neural Network Attributions: A Causal Perspective , 2019, ICML.

[188]  Zhang Yi,et al.  Automated diagnosis of breast ultrasonography images using deep neural networks , 2019, Medical Image Anal..

[189]  Jared A. Dunnmon,et al.  Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. , 2019, Radiology.

[190]  Alexander Wong,et al.  SISC: End-to-End Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells , 2019, IEEE Access.

[191]  S Ourselin,et al.  Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study , 2019, United European gastroenterology journal.

[192]  Marleen de Bruijne,et al.  Enlarged perivascular spaces in brain MRI: Automated quantification in four regions , 2019, NeuroImage.

[193]  J. H. Kim,et al.  Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography , 2019, Investigative radiology.

[194]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[195]  Daniel Rueckert,et al.  Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging , 2019, IEEE Transactions on Medical Imaging.

[196]  Shunxing Bao,et al.  Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning , 2019, Medical Imaging: Image Processing.

[197]  Lei Wang,et al.  SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images , 2018, Comput. Medical Imaging Graph..

[198]  Nils Gessert,et al.  Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization , 2018, Medical Imaging: Image Processing.

[199]  Gustavo Carneiro,et al.  Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI , 2018, Medical Image Anal..

[200]  Ben Glocker,et al.  Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images , 2018, Medical Image Anal..

[201]  Cynthia Rudin,et al.  This Looks Like That: Deep Learning for Interpretable Image Recognition , 2018 .

[202]  Denise R. Aberle,et al.  An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification , 2018, Expert Syst. Appl..

[203]  Josien P. W. Pluim,et al.  Not‐so‐supervised: A survey of semi‐supervised, multi‐instance, and transfer learning in medical image analysis , 2018, Medical Image Anal..

[204]  Marleen de Bruijne,et al.  3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI , 2018, Medical Image Anal..

[205]  Jia-Hong Gao,et al.  Decoding and mapping task states of the human brain via deep learning , 2018, Human brain mapping.

[206]  Emanuele Pesce,et al.  Learning to detect chest radiographs containing pulmonary lesions using visual attention networks , 2017, Medical Image Anal..

[207]  Zhe Zhu,et al.  Deep Learning for identifying radiogenomic associations in breast cancer , 2017, Comput. Biol. Medicine.

[208]  Alexander Wong,et al.  Discovery Radiomics With CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy , 2017, IEEE Access.

[209]  Kato Yoko,et al.  Automated Stenosis Detection and Classification in X-ray Angiography Using Deep Neural Network , 2019 .

[210]  N. Rekhtman,et al.  Predictive Markers , 2019, Quick Reference Handbook for Surgical Pathologists.

[211]  Xun Jia,et al.  Clinical implementation of AI technologies will require interpretable AI models. , 2019, Medical physics.

[212]  Hiroshi Fujita,et al.  Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network , 2019, Informatics in Medicine Unlocked.

[213]  Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, California, United States, 16-21 February 2019 , 2019, Medical Imaging: Computer-Aided Diagnosis.

[214]  Jiacai Zhang,et al.  Decoding Behavior Tasks From Brain Activity Using Deep Transfer Learning , 2019, IEEE Access.

[215]  Yang Jia,et al.  Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment , 2018, J. Electr. Comput. Eng..

[216]  Mohammad Mansouri,et al.  An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets , 2018, Nature Biomedical Engineering.

[217]  Rui Xu,et al.  A Pathology Image Diagnosis Network with Visual Interpretability and Structured Diagnostic Report , 2018, ICONIP.

[218]  Wesley De Neve,et al.  Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[219]  Alexander D. Weston,et al.  What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images. , 2018, AJR. American journal of roentgenology.

[220]  A. Ng,et al.  Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet , 2018, PLoS medicine.

[221]  Evgeny Burnaev,et al.  Voxelwise 3D Convolutional and Recurrent Neural Networks for Epilepsy and Depression Diagnostics from Structural and Functional MRI Data , 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

[222]  R. Gillies,et al.  Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study , 2018, PLoS medicine.

[223]  A. Ng,et al.  Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists , 2018, PLoS medicine.

[224]  Michael J. Keiser,et al.  Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline , 2018, Nature Communications.

[225]  Imane Allaouzi,et al.  Automatic Caption Generation for Medical Images , 2018, SCA.

[226]  Been Kim,et al.  Sanity Checks for Saliency Maps , 2018, NeurIPS.

[227]  Stefan Wermter,et al.  Classification of MRI Migraine Medical Data Using 3D Convolutional Neural Network , 2018, ICANN.

[228]  Jaime S. Cardoso,et al.  Towards Complementary Explanations Using Deep Neural Networks , 2018, MLCN/DLF/iMIMIC@MICCAI.

[229]  Victor Alves,et al.  Understanding and Interpreting Machine Learning in Medical Image Computing Applications , 2018, Lecture Notes in Computer Science.

[230]  Amina Adadi,et al.  Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.

[231]  Zhongchao Shi,et al.  A Diagnostic Report Generator from CT Volumes on Liver Tumor with Semi-supervised Attention Mechanism , 2018, MICCAI.

[232]  Christopher D. Manning,et al.  Learning to Summarize Radiology Findings , 2018, Louhi@EMNLP.

[233]  Vanathi Gopalakrishnan,et al.  A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks , 2018, NeuroImage.

[234]  Theerawit Wilaiprasitporn,et al.  Automatic Lung Cancer Prediction from Chest X-ray Images Using the Deep Learning Approach , 2018, 2018 11th Biomedical Engineering International Conference (BMEiCON).

[235]  Dwarikanath Mahapatra,et al.  Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images , 2018, MLMI@MICCAI.

[236]  Li Liu,et al.  Breast mass classification via deeply integrating the contextual information from multi-view data , 2018, Pattern Recognit..

[237]  Linda G. Shapiro,et al.  Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks , 2018, Pattern Recognit..

[238]  Barnabás Póczos,et al.  Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector , 2018, MICCAI.

[239]  Sang Jun Park,et al.  Laterality Classification of Fundus Images Using Interpretable Deep Neural Network , 2018, Journal of Digital Imaging.

[240]  John R. Smith,et al.  Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images , 2018, MLCN/DLF/iMIMIC@MICCAI.

[241]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[242]  Philip H. S. Torr,et al.  Learn To Pay Attention , 2018, ICLR.

[243]  Jianwei Wang,et al.  Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[244]  Ronald M. Summers,et al.  TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[245]  Martin Wattenberg,et al.  Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) , 2017, ICML.

[246]  Ronald M. Summers,et al.  Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[247]  Ender Konukoglu,et al.  Visual Feature Attribution Using Wasserstein GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[248]  Pengtao Xie,et al.  On the Automatic Generation of Medical Imaging Reports , 2017, ACL.

[249]  Wei Wei,et al.  Thoracic Disease Identification and Localization with Limited Supervision , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[250]  Yan Liu,et al.  Detecting Statistical Interactions from Neural Network Weights , 2017, ICLR.

[251]  Victor Alves,et al.  Understanding and Interpreting Machine Learning in Medical Image Computing Applications , 2018, Lecture Notes in Computer Science.

[252]  Hong Ji,et al.  Deep Learning Features for Lung Adenocarcinoma Classification with Tissue Pathology Images , 2017, ICONIP.

[253]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[254]  Andrew Zisserman,et al.  SpineNet: Automated classification and evidence visualization in spinal MRIs , 2017, Medical Image Anal..

[255]  Xin Yang,et al.  Joint Detection and Diagnosis of Prostate Cancer in Multi-parametric MRI Based on Multimodal Convolutional Neural Networks , 2017, MICCAI.

[256]  Lin Yang,et al.  TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References , 2017, MICCAI.

[257]  Lin Yang,et al.  MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[258]  Saeed Hassanpour,et al.  Looking Under the Hood: Deep Neural Network Visualization to Interpret Whole-Slide Image Analysis Outcomes for Colorectal Polyps , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[259]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[260]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[261]  Bin Yu,et al.  Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning , 2017, ArXiv.

[262]  Andrea Vedaldi,et al.  Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[263]  Percy Liang,et al.  Understanding Black-box Predictions via Influence Functions , 2017, ICML.

[264]  Been Kim,et al.  Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.

[265]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[266]  Max Welling,et al.  Visualizing Deep Neural Network Decisions: Prediction Difference Analysis , 2017, ICLR.

[267]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[268]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[269]  Ole Winther,et al.  Ladder Variational Autoencoders , 2016, NIPS.

[270]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[271]  Alexander Binder,et al.  On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.

[272]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[273]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[274]  Nir Ailon,et al.  Deep Metric Learning Using Triplet Network , 2014, SIMBAD.

[275]  C. Lawrence Zitnick,et al.  CIDEr: Consensus-based image description evaluation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[276]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[277]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[278]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[279]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[280]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[281]  Ali Kashif Bashir,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2013, ICIRA 2013.

[282]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[283]  Febo Cincotti,et al.  2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) , 2011, EMBC 2011.

[284]  Daniel Rueckert,et al.  Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II , 2017, Lecture Notes in Computer Science.

[285]  Marko Robnik-Sikonja,et al.  Explaining Classifications For Individual Instances , 2008, IEEE Transactions on Knowledge and Data Engineering.

[286]  Alon Lavie,et al.  METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.

[287]  Jean Carletta,et al.  Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization , 2005, ACL 2005.

[288]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[289]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[290]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[291]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[292]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[293]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[294]  Deborah Silver,et al.  Feature Visualization , 1994, Scientific Visualization.

[295]  L. Shapley.  A Value for n-person Games , 1988 .

[296]  Ashish Kumar,et al.  Generation, analysis and evaluation of bi-phase complementary pairs , 1970 .

[297]  S. Weisberg,et al.  Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression , 1980 .

[298]  Illtyd Trethowan.  Causality , 1938 .