medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space

medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space. Dravid, A., Schiffers, F., Gong, B., & Katsaggelos, A. K. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), volume 2022-June, pages 2935–2944, jun, 2022. IEEE.

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Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space where there are severe potential repercussions, we need to develop methods to gain confidence in the models' decisions. To this end, we propose a novel medical imaging generative adversarial framework, medXGAN (medical eXplanation GAN), to visually explain what a medical classifier focuses on in its binary predictions. By encoding domain knowledge of medical images, we are able to disentangle anatomical structure and pathology, leading to fine-grained visualization through latent interpolation. Furthermore, we optimize the latent space such that interpolation explains how the features contribute to the classifier's output. Our method outperforms baselines such as Gradient-Weighted Class Activation Mapping (Grad-CAM) and Integrated Gradients in localization and explanatory ability. Additionally, a combination of the medXGAN with Integrated Gradients can yield explanations more robust to noise. The project page with code is available at: https://avdravid.github.io/medXGANpage/.

@inproceedings{dravid2022medxgan,
abstract = {Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space where there are severe potential repercussions, we need to develop methods to gain confidence in the models' decisions. To this end, we propose a novel medical imaging generative adversarial framework, medXGAN (medical eXplanation GAN), to visually explain what a medical classifier focuses on in its binary predictions. By encoding domain knowledge of medical images, we are able to disentangle anatomical structure and pathology, leading to fine-grained visualization through latent interpolation. Furthermore, we optimize the latent space such that interpolation explains how the features contribute to the classifier's output. Our method outperforms baselines such as Gradient-Weighted Class Activation Mapping (Grad-CAM) and Integrated Gradients in localization and explanatory ability. Additionally, a combination of the medXGAN with Integrated Gradients can yield explanations more robust to noise. The project page with code is available at: https://avdravid.github.io/medXGANpage/.},
archivePrefix = {arXiv},
arxivId = {2204.05376},
author = {Dravid, Amil and Schiffers, Florian and Gong, Boqing and Katsaggelos, Aggelos K.},
booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
doi = {10.1109/CVPRW56347.2022.00331},
eprint = {2204.05376},
isbn = {978-1-6654-8739-9},
issn = {21607516},
month = {jun},
pages = {2935--2944},
publisher = {IEEE},
title = {{medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space}},
url = {https://ieeexplore.ieee.org/document/9857306/},
volume = {2022-June},
year = {2022}
}

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