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

• URL: http://arxiv.org/abs/2204.05376v1
• Date: Mon, 11 Apr 2022 19:27:15 GMT
• Title: medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
• Abstract summary: We propose a novel medical imaging generative framework, medXGAN, to explain what a medical adversarial 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. Our method outperforms baselines such as Gradient-Weighted Class Activation Mapping (Grad-CAM) and Integrated Gradients in localization and explanatory ability.
• Score: 38.57696431713717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 code is available at: https://github.com/avdravid/medXGAN_explanations.

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