Finding a Needle in the Haystack: Attention-Based Classification of High Resolution Microscopy Images
暂无分享,去创建一个
Saeed Hassanpour | Behnaz Abdollahi | Arief Suriawinata | Naofumi Tomita | Jason Wei | Bing Ren | S. Hassanpour | Jason Wei | B. Abdollahi | A. Suriawinata | Bing Ren | Naofumi Tomita
[1] Leslie N. Smith,et al. Cyclical Learning Rates for Training Neural Networks , 2015, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[2] Aleksey Boyko,et al. Detecting Cancer Metastases on Gigapixel Pathology Images , 2017, ArXiv.
[3] Saeed Hassanpour,et al. Deep Learning for Classification of Colorectal Polyps on Whole-slide Images , 2017, Journal of pathology informatics.
[4] Jiebo Luo,et al. Image Captioning with Semantic Attention , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] H. Adami,et al. A global assessment of the oesophageal adenocarcinoma epidemic , 2012, Gut.
[6] Yi Yang,et al. Attention to Scale: Scale-Aware Semantic Image Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Wolzt,et al. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2003, The Journal of the American College of Dentists.
[8] R. Haggitt,et al. Barrett's esophagus, dysplasia, and adenocarcinoma. , 1994, Human pathology.
[9] Tao Mei,et al. Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Ehsan Kazemi,et al. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images , 2017, bioRxiv.
[11] A. Polednak,et al. Trends in survival for both histologic types of esophageal cancer in U.S. surveillance, epidemiology and end results areas , 2003, International journal of cancer.
[12] Aristotelis Tsirigos,et al. Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning , 2017, bioRxiv.
[13] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[14] R. Goyal,et al. The histologic spectrum of Barrett's esophagus. , 1976, The New England journal of medicine.
[15] Giovanni Montana,et al. Learning to detect chest radiographs containing lung nodules using visual attention networks , 2017, ArXiv.
[16] Gregory Y. Lauwers,et al. Interobserver Variability in the Diagnosis of Crypt Dysplasia in Barrett Esophagus , 2011, The American journal of surgical pathology.
[17] J. Goldblum,et al. Odze and Goldblum Surgical Pathology of the GI Tract, Liver, Biliary Tract and Pancreas , 2014 .
[18] Saeed Hassanpour,et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks , 2019, Scientific Reports.
[19] Joel H. Saltz,et al. Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Giovanni Montana,et al. Learning what to look in chest X-rays with a recurrent visual attention model , 2017, ArXiv.
[21] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[22] M F Dixon,et al. The Vienna classification of gastrointestinal epithelial neoplasia , 2000, Gut.
[23] Christopher P. Wild,et al. Reflux, Barrett's oesophagus and adenocarcinoma: burning questions , 2003, Nature Reviews Cancer.
[24] Anant Madabhushi,et al. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images , 2016, Neurocomputing.
[25] C. Gutschow,et al. Demographic variations in the rising incidence of esophageal adenocarcinoma in white males , 2001, Cancer.
[26] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[27] G. Lapertosa,et al. Risk factors for Barrett's esophagus: A case‐control study , 2002, International journal of cancer.
[28] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Akira Saito,et al. Automated gastric cancer diagnosis on H&E-stained sections; ltraining a classifier on a large scale with multiple instance machine learning , 2013, Medical Imaging.
[30] Wei-Hung Weng,et al. Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval , 2017, ArXiv.
[31] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[32] 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).
[33] Xiaogang Wang,et al. Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Emanuele Pesce,et al. Learning to detect chest radiographs containing pulmonary lesions using visual attention networks , 2017, Medical Image Anal..
[35] Chandan Chakraborty,et al. Efficient deep learning model for mitosis detection using breast histopathology images , 2017, Comput. Medical Imaging Graph..
[36] 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).
[37] Anant Madabhushi,et al. Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features , 2017, Journal of medical imaging.
[38] J. Siewert,et al. Barrett's esophagus: Pathogenesis, epidemiology, functional abnormalities, malignant degeneration, and surgical management , 2005, Dysphagia.
[39] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[40] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[41] Yi Yang,et al. Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification , 2018, ArXiv.
[42] L. Karnell,et al. National Cancer Data Base report on esophageal carcinoma , 1996, Cancer.
[43] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[44] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[45] L. Brown,et al. Epidemiologic trends in esophageal and gastric cancer in the United States. , 2002, Surgical oncology clinics of North America.
[46] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[47] Saeed Hassanpour,et al. Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach , 2019, Journal of pathology informatics.
[48] Daisuke Komura,et al. Machine Learning Methods for Histopathological Image Analysis , 2017, Computational and structural biotechnology journal.
[49] Jérémie F. Cohen,et al. STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. , 2015, Radiology.
[50] Blot Wj,et al. Esophageal cancer trends and risk factors. , 1994 .