MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images
暂无分享,去创建一个
Qiang Chen | Sijie Niu | Xiao Ma | Zexuan Ji | Daniel L Rubin | Theodore Leng | D. Rubin | Sijie Niu | Zexuan Ji | Xiao Ma | T. Leng | Qiang Chen
[1] Ivan Laptev,et al. Is object localization for free? - Weakly-supervised learning with convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Steffen Schmitz-Valckenberg,et al. Geographic atrophy: clinical features and potential therapeutic approaches. , 2014, Ophthalmology.
[3] James T. Handa,et al. AUTOMATED IMAGE ALIGNMENT AND SEGMENTATION TO FOLLOW PROGRESSION OF GEOGRAPHIC ATROPHY IN AGE-RELATED MACULAR DEGENERATION , 2014, Retina.
[4] Qiang Chen,et al. Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images , 2018, Translational vision science & technology.
[5] In-So Kweon,et al. CBAM: Convolutional Block Attention Module , 2018, ECCV.
[6] Christophe Panthier,et al. EVALUATION OF SEMIAUTOMATED MEASUREMENT OF GEOGRAPHIC ATROPHY IN AGE-RELATED MACULAR DEGENERATION BY FUNDUS AUTOFLUORESCENCE IN CLINICAL SETTING , 2014, Retina.
[7] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[8] Steffen Schmitz-Valckenberg,et al. High-resolution spectral domain-OCT imaging in geographic atrophy associated with age-related macular degeneration. , 2008, Investigative ophthalmology & visual science.
[9] Qiang Chen,et al. Network In Network , 2013, ICLR.
[10] Lei Guo,et al. Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[11] Glenn J Jaffe,et al. Consensus Definition for Atrophy Associated with Age-Related Macular Degeneration on OCT: Classification of Atrophy Report 3. , 2017, Ophthalmology.
[12] Qiang Chen,et al. Automated drusen segmentation and quantification in SD-OCT images , 2013, Medical Image Anal..
[13] Qiang Chen,et al. A Multi-Scale Deep Convolutional Neural Network For Joint Segmentation And Prediction Of Geographic Atrophy In SD-OCT Images , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[14] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] P. Jong. Prevalence of age-related macular degeneration in the United States. , 2004 .
[16] Zhe L. Lin,et al. Top-Down Neural Attention by Excitation Backprop , 2016, International Journal of Computer Vision.
[17] Ivan Laptev,et al. Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Thomas Ach,et al. The Project MACULA Retinal Pigment Epithelium Grading System for Histology and Optical Coherence Tomography in Age-Related Macular Degeneration. , 2015, Investigative ophthalmology & visual science.
[19] Vladlen Koltun,et al. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.
[20] Bram van Ginneken,et al. A deep learning model for segmentation of geographic atrophy to study its long-term natural history , 2019, ArXiv.
[21] Lars Petersson,et al. Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation , 2016, ECCV.
[22] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Xiaogang Wang,et al. Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] M. Tolentino,et al. Drugs in Phase II clinical trials for the treatment of age-related macular degeneration , 2015, Expert opinion on investigational drugs.
[25] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[26] 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.
[27] Karen O. Egiazarian,et al. Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms , 2012, IEEE Transactions on Image Processing.
[28] Dong Xu,et al. Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey , 2018, IEEE Signal Processing Magazine.
[29] Philippe Burlina,et al. Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images , 2015, Comput. Biol. Medicine.
[30] D. Rubin,et al. Semi-automatic geographic atrophy segmentation for SD-OCT images. , 2013, Biomedical optics express.
[31] Qiang Chen,et al. Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor. , 2016, Biomedical optics express.
[32] Yunchao Wei,et al. Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Leo Grady,et al. Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Deyu Meng,et al. Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Qiang Qiu,et al. Weakly Supervised Instance Segmentation Using Class Peak Response , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] Jianmin Jiang,et al. A Simple Pooling-Based Design for Real-Time Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Kun Gao,et al. Multi-path 3D Convolution Neural Network for Automated Geographic Atrophy Segmentation in SD-OCT Images , 2018, ICIC.
[39] Zhihong Hu,et al. Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images. , 2013, Investigative ophthalmology & visual science.
[40] Benita J. O’Colmain,et al. Prevalence of age-related macular degeneration in the United States. , 2004, Archives of ophthalmology.
[41] Zhi-Hua Zhou,et al. A brief introduction to weakly supervised learning , 2018 .
[42] Ohad Shamir,et al. The Power of Depth for Feedforward Neural Networks , 2015, COLT.
[43] Vineeth N. Balasubramanian,et al. Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[44] A Hofman,et al. Age-specific prevalence and causes of blindness and visual impairment in an older population: the Rotterdam Study. , 1998, Archives of ophthalmology.
[45] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Matthieu Cord,et al. WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Yizhou Yu,et al. FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation , 2019, ArXiv.
[48] Ying Chen,et al. M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network , 2018, AAAI.
[49] Yi Yang,et al. Multi-scale discriminative Region Discovery for Weakly-Supervised Object Localization , 2019, ArXiv.
[50] Xuelong Li,et al. A Review of Co-Saliency Detection Algorithms , 2018 .
[51] Gernot A. Fink,et al. Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[52] Junwei Han,et al. Synthesizing Supervision for Learning Deep Saliency Network without Human Annotation , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).