Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns
暂无分享,去创建一个
Jan S. Kirschke | Stefan Braunewell | Matthias Kohl | Benedikt Wiestler | Maximilian Möller | Marie Piraud | Björn H. Menze | Florian Kofler | Bjorn H. Menze | M. Piraud | B. Wiestler | J. Kirschke | F. Kofler | S. Braunewell | Maximilian Möller | Matthias Kohl
[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] Been Kim,et al. Sanity Checks for Saliency Maps , 2018, NeurIPS.
[3] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[4] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[5] Ronan Collobert,et al. From image-level to pixel-level labeling with Convolutional Networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] 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.
[7] Zachary C. Lipton,et al. The mythos of model interpretability , 2018, Commun. ACM.
[8] Bolei Zhou,et al. Object Detectors Emerge in Deep Scene CNNs , 2014, ICLR.
[9] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Sébastien Ourselin,et al. Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks , 2017, BrainLes@MICCAI.
[11] Zachary Chase Lipton. The mythos of model interpretability , 2016, ACM Queue.
[12] Qiang Qiu,et al. Weakly Supervised Instance Segmentation Using Class Peak Response , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.