Resolving challenges in deep learning-based analyses of
histopathological images using explanation methods

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

15 August 2019

Sebastian Lapuschkin

Frederick Klauschen

Alexander Binder

Papers citing "Resolving challenges in deep learning-based analyses of histopathological images using explanation methods"

16 / 16 papers shown

Title
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks Lorenz Linhardt Klaus-Robert Muller G. Montavon AAML 23 7 0 12 Apr 2023
From slides (through tiles) to pixels: an explainability framework for weakly supervised models in pre-clinical pathology Marco Bertolini Van-Khoa Le Jake Pencharz A. Poehlmann Djork-Arné Clevert Santiago D. Villalba F. Montanari 28 0 0 03 Feb 2023
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Pattarawat Chormai J. Herrmann Klaus-Robert Muller G. Montavon FAtt 45 17 0 30 Dec 2022
Attention-based Interpretable Regression of Gene Expression in Histology Mara Graziani Niccolo Marini Nicolas Deutschmann Nikita Janakarajan Henning Muller María Rodríguez Martínez MedIm 38 5 0 29 Aug 2022
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI Sami Ede Serop Baghdadlian Leander Weber A. Nguyen Dario Zanca Wojciech Samek Sebastian Lapuschkin CLL 27 6 0 04 May 2022
Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery A. Osio Hoàng-Ân Lê Samson Ayugi F. Onyango P. Odwe Sébastien Lefèvre 11 2 0 14 Apr 2022
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology Maximilian Springenberg A. Frommholz M. Wenzel Eva Weicken Jackie Ma Nils Strodthoff MedIm 25 42 0 11 Apr 2022
Explaining Bayesian Neural Networks Kirill Bykov Marina M.-C. Höhne Adelaida Creosteanu Klaus-Robert Muller Frederick Klauschen Shinichi Nakajima Marius Kloft BDL AAML 31 25 0 23 Aug 2021
Quantifying Explainers of Graph Neural Networks in Computational Pathology Guillaume Jaume Pushpak Pati Behzad Bozorgtabar Antonio Foncubierta-Rodríguez Florinda Feroce A. Anniciello T. Rau Jean-Philippe Thiran M. Gabrani O. Goksel FAtt 23 76 0 25 Nov 2020
It's All in the Name: A Character Based Approach To Infer Religion Rochana Chaturvedi Sugat Chaturvedi 19 23 0 27 Oct 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 24 31 0 16 Jun 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 Mar 2020
Towards Best Practice in Explaining Neural Network Decisions with LRP M. Kohlbrenner Alexander Bauer Shinichi Nakajima Alexander Binder Wojciech Samek Sebastian Lapuschkin 14 148 0 22 Oct 2019
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey Jérôme Rony Soufiane Belharbi Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 25 70 0 08 Sep 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 286 10,613 0 19 Feb 2017