A survey on incorporating domain knowledge into deep learning for medical image analysis

[1]  W. Stolz,et al.  The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.

[2]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[5]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[6]  John K. Tsotsos,et al.  Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI , 2008, Medical Image Anal..

[7]  Jason Weston,et al.  Curriculum learning , 2009, ICML '09.

[8]  Peter Norvig,et al.  The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.

[9]  Rahul Dave,et al.  VARIOUS TYPES AND MANAGEMENT OF BREAST CANCER: AN OVERVIEW , 2010, Journal of advanced pharmaceutical technology & research.

[10]  Daphne Koller,et al.  Self-Paced Learning for Latent Variable Models , 2010, NIPS.

[11]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Charles E. Kahn,et al.  Content Analysis of Reporting Templates and Free-Text Radiology Reports , 2013, Journal of Digital Imaging.

[15]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[16]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[17]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[18]  Zhen Wang,et al.  Knowledge Graph and Text Jointly Embedding , 2014, EMNLP.

[19]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Bram van Ginneken,et al.  Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box , 2015, Medical Image Anal..

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

[22]  Ronald M. Summers,et al.  Deep convolutional networks for pancreas segmentation in CT imaging , 2015, Medical Imaging.

[23]  Hayit Greenspan,et al.  A comparative study for chest radiograph image retrieval using binary texture and deep learning classification , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[24]  Hayit Greenspan,et al.  Deep learning with non-medical training used for chest pathology identification , 2015, Medical Imaging.

[25]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[26]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[28]  Bram van Ginneken,et al.  Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks , 2016, IEEE Transactions on Medical Imaging.

[29]  Jae Y. Shin,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE transactions on medical imaging.

[30]  Sidong Liu,et al.  Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features , 2016, MCV/BAMBI@MICCAI.

[31]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Hao Chen,et al.  Deep Contextual Networks for Neuronal Structure Segmentation , 2016, AAAI.

[33]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[34]  Seyed-Ahmad Ahmadi,et al.  Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields , 2016, MICCAI.

[35]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[36]  Hoo-Chang Hoo-Chang Shin Shin,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, Ieee Transactions on Medical Imaging.

[37]  Hongzhi Wang,et al.  A hybrid learning approach for semantic labeling of cardiac CT slices and recognition of body position , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[38]  B Huynh,et al.  MO-DE-207B-06: Computer-Aided Diagnosis of Breast Ultrasound Images Using Transfer Learning From Deep Convolutional Neural Networks. , 2016, Medical physics.

[39]  Hyo-Eun Kim,et al.  Self-Transfer Learning for Weakly Supervised Lesion Localization , 2016, MICCAI.

[40]  Ulas Bagci,et al.  Characterization of Lung Nodule Malignancy Using Hybrid Shape and Appearance Features , 2016, MICCAI.

[41]  Hayit Greenspan,et al.  Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval , 2016, SPIE Medical Imaging.

[42]  Hiroyuki Yoshida,et al.  Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography , 2016, SPIE Medical Imaging.

[43]  Michael I. Jordan,et al.  Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.

[44]  Andreas Uhl,et al.  Convolutional Neural Network Architectures for the Automated Diagnosis of Celiac Disease , 2016, CARE@MICCAI.

[45]  Max A. Viergever,et al.  Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities , 2016, MICCAI.

[46]  Hui Li,et al.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.

[47]  Hao Chen,et al.  Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images , 2016, MICCAI.

[48]  Phi Vu Tran,et al.  A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI , 2016, ArXiv.

[49]  Sule Yildirim Yayilgan,et al.  Combining deep learning and hand-crafted features for skin lesion classification , 2016, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[50]  Ghassan Hamarneh,et al.  Topology Aware Fully Convolutional Networks for Histology Gland Segmentation , 2016, MICCAI.

[51]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  Guoyan Zheng,et al.  3D U-net with Multi-level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images , 2017, MLMI@MICCAI.

[53]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[54]  Nico Karssemeijer,et al.  Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation , 2017, MICCAI.

[55]  Jun Zhao,et al.  Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features , 2017, Medical Imaging.

[56]  Hariharan Ravishankar,et al.  Learning and Incorporating Shape Models for Semantic Segmentation , 2017, MICCAI.

[57]  Carmen C. Y. Poon,et al.  Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain , 2017, IEEE Journal of Biomedical and Health Informatics.

[58]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

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

[60]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[61]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Aneta Lisowska,et al.  Context-Aware Convolutional Neural Networks for Stroke Sign Detection in Non-contrast CT Scans , 2017, MIUA.

[63]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[64]  B. Erickson,et al.  Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[65]  Dean C. Barratt,et al.  Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks , 2017, MICCAI.

[66]  Jie-Zhi Cheng,et al.  Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images. , 2017, IEEE transactions on medical imaging.

[67]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[68]  Wei Guo,et al.  Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians , 2017, ArXiv.

[69]  Kenji Suzuki.  Survey of Deep Learning Applications to Medical Image Analysis , 2017 .

[70]  Lei Zhang,et al.  Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[71]  Wei Zeng,et al.  Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer , 2017, ArXiv.

[72]  A. Ng,et al.  MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs. , 2017 .

[73]  Aneta Lisowska,et al.  Thrombus Detection in CT Brain Scans using a Convolutional Neural Network , 2017, BIOIMAGING.

[74]  Hao Chen,et al.  Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge , 2016, Medical Image Anal..

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

[76]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[77]  Vipin Chaudhary,et al.  Intervertebral disc detection in X-ray images using faster R-CNN , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[78]  Sung Wook Baik,et al.  SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs , 2017, PloS one.

[79]  Daguang Xu,et al.  Automatic Liver Segmentation Using an Adversarial Image-to-Image Network , 2017, MICCAI.

[80]  Natalia Antropova,et al.  A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets , 2017, Medical physics.

[81]  Hao Chen,et al.  DCAN: Deep contour‐aware networks for object instance segmentation from histology images , 2017, Medical Image Anal..

[82]  Guy Amit,et al.  Classification of breast lesions using cross-modal deep learning , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[83]  Leonid Karlinsky,et al.  Domain specific convolutional neural nets for detection of architectural distortion in mammograms , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[84]  Anant Madabhushi,et al.  Intra-perinodular Textural Transition (Ipris): A 3D Descriptor for Nodule Diagnosis on Lung CT , 2017, MICCAI.

[85]  Sebastian J. Schlecht,et al.  Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks , 2017, ArXiv.

[86]  J. Morris,et al.  The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement , 2017, Alzheimer's & Dementia.

[87]  Berkman Sahiner,et al.  Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks , 2017, Medical physics.

[88]  Konstantinos Kamnitsas,et al.  Unsupervised domain adaptation in brain lesion segmentation with adversarial networks , 2016, IPMI.

[89]  Kisung Lee,et al.  Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID) , 2017, BCB.

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

[91]  Lubomir M. Hadjiiski,et al.  Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms , 2017, Physics in medicine and biology.

[92]  Joel H. Saltz,et al.  Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[93]  Nicolas Guizard,et al.  CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance , 2017, MICCAI.

[94]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[95]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[96]  Ulas Bagci,et al.  Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning , 2017, IPMI.

[97]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[98]  Purang Abolmaesumi,et al.  Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection , 2017, International Journal of Computer Assisted Radiology and Surgery.

[99]  Xin Yang,et al.  Fine-Grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images , 2016, AAAI.

[100]  Pabitra Mitra,et al.  Generative Adversarial Learning for Reducing Manual Annotation in Semantic Segmentation on Large Scale Miscroscopy Images: Automated Vessel Segmentation in Retinal Fundus Image as Test Case , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[101]  Pheng-Ann Heng,et al.  Cascaded Fully Convolutional Networks for automatic prenatal ultrasound image segmentation , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[102]  Ghassan Hamarneh,et al.  Generative adversarial networks to segment skin lesions , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[103]  Guang Yang,et al.  DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.

[104]  Gustavo Carneiro,et al.  Training Medical Image Analysis Systems like Radiologists , 2018, MICCAI.

[105]  Hao Chen,et al.  Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation , 2018, MLMI@MICCAI.

[106]  Ming-Hsuan Yang,et al.  Learning to Adapt Structured Output Space for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[107]  Sébastien Ourselin,et al.  An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation , 2017, MIUA.

[108]  Zhe Zhu,et al.  Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression , 2018, Medical Imaging.

[109]  Heng-Da Cheng,et al.  Medical Knowledge Constrained Semantic Breast Ultrasound Image Segmentation , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[110]  Vijayan K. Asari,et al.  Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation , 2018, ArXiv.

[111]  et al.,et al.  Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.

[112]  Konstantinos Kamnitsas,et al.  Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation , 2017, IEEE Transactions on Medical Imaging.

[113]  Joseph O. Deasy,et al.  Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation , 2018, MICCAI.

[114]  Dinggang Shen,et al.  Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning , 2018, MICCAI.

[115]  Yin-Yin Liao,et al.  Breast tumor classification using different features of quantitative ultrasound parametric images , 2019, International Journal of Computer Assisted Radiology and Surgery.

[116]  Yanning Zhang,et al.  Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT , 2018, Inf. Fusion.

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

[118]  Gustavo Carneiro,et al.  Producing radiologist-quality reports for interpretable artificial intelligence , 2018, ArXiv.

[119]  Xiaohui Xie,et al.  DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[120]  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).

[121]  Daniel Rueckert,et al.  A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[122]  Tolga Çukur,et al.  Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI , 2018, ArXiv.

[123]  Qi Song,et al.  Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis , 2018, MICCAI.

[124]  Mert R. Sabuncu,et al.  Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[125]  Lin Yang,et al.  Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[126]  Taesung Park,et al.  CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.

[127]  Reyer Zwiggelaar,et al.  Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks , 2018, IEEE Journal of Biomedical and Health Informatics.

[128]  Le Lu,et al.  DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning , 2018, Journal of medical imaging.

[129]  Yi Yang,et al.  Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification , 2018, ArXiv.

[130]  Yifei Lu,et al.  Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation , 2018, MICCAI.

[131]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[132]  Ghassan Hamarneh,et al.  Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation , 2018, MICCAI.

[133]  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.

[134]  Yuxing Tang,et al.  Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs , 2018, MLMI@MICCAI.

[135]  Ben Glocker,et al.  Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[136]  Xavier Lladó,et al.  Automated sub‐cortical brain structure segmentation combining spatial and deep convolutional features , 2017, Medical Image Anal..

[137]  Robert Sabourin,et al.  Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images , 2018, ICIAR.

[138]  Saeid Nahavandi,et al.  A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval , 2018, Expert Syst. Appl..

[139]  Eric P. Xing,et al.  Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation , 2018, NeurIPS.

[140]  Hongyan Liu,et al.  Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models , 2018, Knowl. Based Syst..

[141]  Yujun Liu,et al.  One stage lesion detection based on 3D context convolutional neural networks , 2019, Comput. Electr. Eng..

[142]  Weidong Cai,et al.  Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT , 2019, IEEE Transactions on Medical Imaging.

[143]  Yuhang Liu,et al.  From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network , 2019, MICCAI.

[144]  Iván González-Díaz,et al.  DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis , 2019, IEEE Journal of Biomedical and Health Informatics.

[145]  Noel C. F. Codella,et al.  Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 2019, ArXiv.

[146]  Fan Yang,et al.  Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images , 2019, MICCAI.

[147]  Jacob Goldberger,et al.  Classification and Detection in Mammograms With Weak Supervision Via Dual Branch Deep Neural Net , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[148]  Daniel Rueckert,et al.  Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images , 2019, MICCAI.

[149]  Linghong Zhou,et al.  Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification , 2019, European Radiology.

[150]  Joon Young Kim,et al.  CNN-based diagnosis models for canine ulcerative keratitis , 2019, Scientific Reports.

[151]  Zhiming Luo,et al.  Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation , 2019, IEEE Journal of Biomedical and Health Informatics.

[152]  Noha Ghatwary,et al.  Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features , 2019, IEEE Access.

[153]  Wei Li,et al.  Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation , 2019, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[154]  Nassir Navab,et al.  Medical-based Deep Curriculum Learning for Improved Fracture Classification , 2019, MICCAI.

[155]  Hayit Greenspan,et al.  Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection , 2018, Eng. Appl. Artif. Intell..

[156]  Chunfeng Lian,et al.  Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks , 2019, Medical Image Anal..

[157]  Kai Ma,et al.  Med3D: Transfer Learning for 3D Medical Image Analysis , 2019, ArXiv.

[158]  Jianfeng Lu,et al.  Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning , 2019, IEEE Access.

[159]  Chong Wang,et al.  Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification , 2019, IEEE Transactions on Medical Imaging.

[160]  Shadrokh Samavi,et al.  Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[161]  Yi Yang,et al.  Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[162]  Qianqian Du,et al.  DScGANS: Integrate Domain Knowledge in Training Dual-Path Semi-supervised Conditional Generative Adversarial Networks and S3VM for Ultrasonography Thyroid Nodules Classification , 2019, MICCAI.

[163]  R. Joe Stanley,et al.  Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images , 2019, IEEE Journal of Biomedical and Health Informatics.

[164]  Yutaro Iwamoto,et al.  Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior , 2019, MICCAI.

[165]  Yizhou Wang,et al.  MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection , 2019, MICCAI.

[166]  Dorit Merhof,et al.  Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification , 2019, MICCAI.

[167]  Fan Zhang,et al.  Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation , 2019, MICCAI.

[168]  Xiaofei Wang,et al.  Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[169]  Martin Wistuba,et al.  A Survey on Neural Architecture Search , 2019, ArXiv.

[170]  Xiahai Zhuang,et al.  Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors , 2019, MICCAI.

[171]  Joaquim Salvi,et al.  One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks , 2018, NeuroImage: Clinical.

[172]  Enzo Ferrante,et al.  Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders , 2019, MICCAI.

[173]  Daniel Rueckert,et al.  Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[174]  Ghassan Hamarneh,et al.  Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep Learning , 2019, MICCAI.

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

[176]  Demetri Terzopoulos,et al.  End-to-End Boundary Aware Networks for Medical Image Segmentation , 2019, bioRxiv.

[177]  Jiasong Wu,et al.  DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy , 2019, MICCAI.

[178]  Eric Granger,et al.  Curriculum semi-supervised segmentation , 2019, MICCAI.

[179]  Zoe L. Jiang,et al.  Multi-task deep convolutional neural network for cancer diagnosis , 2019, Neurocomputing.

[180]  Lubomir M. Hadjiiski,et al.  Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets , 2019, IEEE Transactions on Medical Imaging.

[181]  Il Dong Yun,et al.  Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images , 2017, IEEE Transactions on Medical Imaging.

[182]  Olivier Bernard,et al.  Cardiac MRI Segmentation with Strong Anatomical Guarantees , 2019, MICCAI.

[183]  Zhengrong Liang,et al.  Expert knowledge-infused deep learning for automatic lung nodule detection. , 2019, Journal of X-ray science and technology.

[184]  Yuanyuan Wang,et al.  The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN , 2019, MICCAI.

[185]  Jun Zhang,et al.  Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics , 2019, IEEE Transactions on Medical Imaging.

[186]  Wenyuan Li,et al.  Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images , 2019, IEEE Transactions on Medical Imaging.

[187]  Yi Pan,et al.  PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks , 2020, ArXiv.

[188]  Hao Chen,et al.  Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation , 2020, IEEE Transactions on Medical Imaging.

[189]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[190]  Xin Huang,et al.  Dual-Ray Net: Automatic Diagnosis of Thoracic Diseases Using Frontal and Lateral Chest X-rays , 2020, J. Medical Imaging Health Informatics.

[191]  Nan Wu,et al.  Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening , 2019, IEEE Transactions on Medical Imaging.

[192]  Jie Shen,et al.  Shape Constrained Network for Eye Segmentation in the Wild , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[193]  Yizhou Yu,et al.  A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images , 2020, European Radiology.

[194]  Adam P. Harrison,et al.  Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network , 2020, MICCAI.

[195]  Hui Cui,et al.  Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-Related Esophageal Fistula Prediction from CT , 2020, MICCAI.

[196]  Joel J. P. C. Rodrigues,et al.  Cascading handcrafted features and Convolutional Neural Network for IoT-enabled brain tumor segmentation , 2020, Comput. Commun..

[197]  Adam P. Harrison,et al.  Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[198]  Tanzila Saba,et al.  Brain tumor detection using fusion of hand crafted and deep learning features , 2020, Cognitive Systems Research.

[199]  V. Burdin,et al.  Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in Mr Images , 2019, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[200]  Lixu Gu,et al.  Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation , 2020, Neurocomputing.

[201]  Hongyu Wang,et al.  A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI. , 2020, Magnetic resonance imaging.

[202]  Kup-Sze Choi,et al.  Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms , 2020, MICCAI.

[203]  Yifan Zhang,et al.  Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis , 2019, IEEE Transactions on Image Processing.

[204]  Rongchang Zhao,et al.  EGDCL: An Adaptive Curriculum Learning Framework for Unbiased Glaucoma Diagnosis , 2020, ECCV.

[205]  Jing Xiao,et al.  MommiNet: Mammographic Multi-view Mass Identification Networks , 2020, MICCAI.

[206]  Alan Yuille,et al.  Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy , 2020, MICCAI.

[207]  Daguang Xu,et al.  When Radiology Report Generation Meets Knowledge Graph , 2020, AAAI.

[208]  Lequan Yu,et al.  Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation , 2020, AAAI.

[209]  Bart Jansen,et al.  Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework , 2020, Medical Imaging.

[210]  Margarida Silveira,et al.  Combining Deep Learning with Handcrafted Features for Cell Nuclei Segmentation * , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[211]  D. D. Pham,et al.  Liver Segmentation in CT with MRI Data: Zero-Shot Domain Adaptation by Contour Extraction and Shape Priors , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[212]  Xiaohong Zhang,et al.  Learning to Recognize Thoracic Disease in Chest X-Rays With Knowledge-Guided Deep Zoom Neural Networks , 2020, IEEE Access.

[213]  Juan Yu,et al.  Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection , 2019, Artif. Intell. Medicine.

[214]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[215]  Xiabi Liu,et al.  A New Three-stage Curriculum Learning Approach for Deep Network Based Liver Tumor Segmentation , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[216]  Lequan Yu,et al.  Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation , 2020, MICCAI.

[217]  Jing Gong,et al.  Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan , 2020, Frontiers in Oncology.

[218]  Shuo Li,et al.  Tripartite-GAN: Synthesizing liver contrast-enhanced MRI to improve tumor detection , 2020, Medical Image Anal..

[219]  Nassir Navab,et al.  Curriculum learning for annotation-efficient medical image analysis: scheduling data with prior knowledge and uncertainty , 2020, ArXiv.

[220]  Yi Pan,et al.  Infant Sound Classification on Multi-stage CNNs with Hybrid Features and Prior Knowledge , 2020, Autonomous Infrastructure, Management and Security.

[221]  Yu Zhang,et al.  Weakly-Supervised Self-Training for Breast Cancer Localization* , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[222]  Robin Lange,et al.  AGAN: An Anatomy Corrector Conditional Generative Adversarial Network , 2020, MICCAI.

[223]  Francis wyffels,et al.  Curriculum Deep Reinforcement Learning with Different Exploration Strategies: A Feasibility Study on Cardiac Landmark Detection , 2020, BIOIMAGING.

[224]  Yizhou Yu,et al.  Cross-View Correspondence Reasoning Based on Bipartite Graph Convolutional Network for Mammogram Mass Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[225]  Isabelle Bloch,et al.  Knowledge Distillation from Multi-modal to Mono-modal Segmentation Networks , 2020, MICCAI.

[226]  Friedhelm Schwenker,et al.  Survey of deep learning in breast cancer image analysis , 2019, Evolving Systems.

[227]  Wang Jun,et al.  Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities , 2020, 2007.06634.

[228]  Pheng-Ann Heng,et al.  CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading , 2019, IEEE Transactions on Medical Imaging.

[229]  Olivier Bernard,et al.  Cardiac Segmentation With Strong Anatomical Guarantees , 2019, IEEE Transactions on Medical Imaging.

[230]  Shuang Yu,et al.  Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling , 2020, MICCAI.

[231]  Lequan Yu,et al.  Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model , 2020, IEEE Transactions on Medical Imaging.

[232]  David Dagan Feng,et al.  Short-Term Lesion Change Detection for Melanoma Screening With Novel Siamese Neural Network , 2020, IEEE Transactions on Medical Imaging.

[233]  Fugen Zhou,et al.  Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images , 2020, IEEE Journal of Biomedical and Health Informatics.

[234]  Sotirios A. Tsaftaris,et al.  Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation , 2020, IEEE Transactions on Medical Imaging.

[235]  Qi Qi,et al.  Curriculum Feature Alignment Domain Adaptation for Epithelium-Stroma Classification in Histopathological Images , 2020, IEEE Journal of Biomedical and Health Informatics.

[236]  Adam P. Harrison,et al.  DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy , 2020, Medical Image Anal..

[237]  Hironobu Fujiyoshi,et al.  Embedding Human Knowledge in Deep Neural Network via Attention Map , 2019, VISIGRAPP.

[238]  Fan Zhang,et al.  PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images , 2020, IEEE Transactions on Medical Imaging.

[239]  Saeed Hassanpour,et al.  Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[240]  Dimitris N. Metaxas,et al.  FocusNetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images , 2020, Medical Image Anal..