Evaluation and comparison of CNN visual explanations for histopathology

[1]  Meyke Hermsen,et al.  1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset , 2018, GigaScience.

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

[3]  Nasir Rajpoot,et al.  PanNuke Dataset Extension, Insights and Baselines , 2020, ArXiv.

[4]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[6]  Henning Müller,et al.  Visualizing and interpreting feature reuse of pretrained CNNs for histopathology , 2019 .

[7]  Henning Müller,et al.  Generalizing convolution neural networks on stain color heterogeneous data for computational pathology , 2020, Medical Imaging: Digital Pathology.

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

[9]  Bolei Zhou,et al.  Interpreting Deep Visual Representations via Network Dissection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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