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Post-hoc analysis is a popular category in eXplainable artificial intelligence (XAI) study.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn L. Ball, Katie S. Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng · 1901
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Note on the location of optimal classifiers in n-dimensional roc space
Ashwin Srinivasan and Ashwin Srinivasan · 1999
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Volume under the roc surface for multi-class problems
César Ferri, José Hernández-Orallo, and Miguel Angel Salido · 2003
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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One weird trick for parallelizing convolutional neural networks
Alex Krizhevsky · 2014
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Striving for simplicity: The all convolutional net, 2014
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Very deep convolutional neural network based image classification using small training sample size
S. Liu and W. Deng · 2015
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Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
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Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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“why should i trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, P. Greenside, A. Shcherbina, and A. Kundaje · 2016
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
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Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K. Müller · 2017
Earlier work this paper cites.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
Cited alongside, same era.
Network dissection: Quantifying interpretability of deep visual representations
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
Testing the robustness of attribution methods for convolutional neural networks in mri-based alzheimer’s disease classification
Fabian Eitel and Kerstin Ritter · 2019
Later among the works it cites.
Guideline-based additive explanation for computer-aided diagnosis of lung nodules
Peifei Zhu and Masahiro Ogino · 2019
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Analyzing Neuroimaging Data Through Recurrent Deep Learning Models
A. W. Thomas, H. R. Heekeren, K. R. Müller, and W. Samek · 2019
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Generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis
Hyebin Lee, Seong Tae Kim, and Yong Man Ro · 2019
Later among the works it cites.
Enhancing the extraction of interpretable information for ischemic stroke imaging from deep neural networks, 2019
Erico Tjoa, Guo Heng, Lu Yuhao, and Cuntai Guan · 2019
Later among the works it cites.
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Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda B. Viégas, and Rory Sayres · 2018
Cited alongside, same era.
Explaining explanations: An overview of interpretability of machine learning
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal · 2018
Cited alongside, same era.
Respond-cam: Analyzing deep models for 3d imaging data by visualizations
Guannan Zhao, Bo Zhou, Kaiwen Wang, Rui Jiang, and Min Xu · 2018
Cited alongside, same era.
Brain biomarker interpretation in asd using deep learning and fmri
Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, and James S. Duncan · 2018
Cited alongside, same era.
Generalizability vs. robustness: Investigating medical imaging networks using adversarial examples
Magdalini Paschali, Sailesh Conjeti, Fernando Navarro, and Nassir Navab · 2018
Cited alongside, same era.
Multiple instance learning for heterogeneous images: Training a cnn for histopathology
Heather D. Couture, J. S. Marron, Charles M. Perou, Melissa A. Troester, and Marc Niethammer · 2018
Cited alongside, same era.
Xiao hui Li, Yuhan Shi, H. Li, Wei Bai, Y. Song, Caleb Chen Cao, and Li-Chiou Chen · 2020
Closest in time.
When explanations lie: Why many modified bp attributions fail
Leon Sixt, Maximilian Granz, and Tim Landgraf · 2020
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There and back again: Revisiting backpropagation saliency methods
S. A. Rebuffi, R. Fong, X. Ji, and A. Vedaldi · 2020
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Understanding the role of individual units in a deep neural network
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba · 2020
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A survey on explainable artificial intelligence (xai): Toward medical xai
Erico Tjoa and Cuntai Guan · 2020
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Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI
Zachary Papanastasopoulos, Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Chintana Paramagul, Mark A. Helvie M.D., and Colleen H. Neal M.D · 2020
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Lrp tutorial, accessed August 16, 2020
Tutorial LRP · 2020
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Swag: Superpixels weighted by average gradients for explanations of cnns
Thomas Hartley, Kirill Sidorov, Christopher Willis, and David Marshall · 2021
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An experimental study of quantitative evaluations on saliency methods
Xiao-Hui Li, Yuhan Shi, Haoyang Li, Wei Bai, Caleb Chen Cao, and Lei Chen · 2021
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Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations
Leila Arras, Ahmed Osman, and Wojciech Samek · 2021
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Xai meets mobile traffic classification: Understanding and improving multimodal deep learning architectures
Alfredo Nascita, Antonio Montieri, Giuseppe Aceto, Domenico Ciuonzo, Valerio Persico, and Antonio Pescapé · 2021
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Interpretable classification of alzheimer’s disease pathologies with a convolutional neural network pipeline
Ziqi Tang, Kangway V. Chuang, Charles DeCarli, Lee-Way Jin, Laurel Beckett, Michael J. Keiser, and Brittany N. Dugger · 2041
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Explainable internet traffic classification
Christian Callegari, Pietro Ducange, Michela Fazzolari, and Massimo Vecchio · 2076
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