论文标题
Medxgan：通过生成潜在空间的医疗分类器的视觉解释
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
论文作者
论文摘要
尽管在过去十年中，深度学习的激增，但由于其黑盒性质，一些用户在实践中持怀疑态度。具体而言，在存在严重潜在影响的医疗空间中，我们需要开发方法以对模型的决策获得信心。为此，我们提出了一种新型的医学成像生成对抗框架Medxgan（Medical Loxpanation GAN），以视觉上解释医学分类器在其二进制预测中的重点。通过编码医学图像的领域知识，我们能够解散解剖结构和病理，从而通过潜在的插值导致细粒度的可视化。此外，我们优化了潜在空间，以便插值解释了该功能如何对分类器的输出贡献。我们的方法的表现优于基线，例如梯度加权类激活映射（GRAD-CAM）和本地化和解释能力的综合梯度。此外，梅德克斯甘（Medxgan）与集成梯度的组合可以使噪声更强大的解释。该代码可在以下网址提供：https：//avdravid.github.io/medxgan_page/。
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://avdravid.github.io/medXGAN_page/.
