让医学图像模型自动生成解释，不降性能还能更可信。
Soft-CAM: Making black box models self-explainable for medical image analysis
- 用卷积层替代全连接层，保留空间信息实现内在可解释性。
- 在三个医学数据集上，解释质量显著优于传统事后分析方法。
- 适合需要可信AI决策的医疗领域，尤其关注模型透明度的研究者。
卷积神经网络（CNN）广泛应用于医疗等高风险场景，常超越人类表现。然而，现有解释方法多为事后归因，依赖对已训练黑箱模型的近似推断，常不稳定、不可靠，难以反映真实推理过程，限制了其在关键应用中的可信度。本文提出SoftCAM，一种简单但高效的方法，使标准CNN架构具备内在可解释性。通过移除全局平均池化层，并将全连接分类层替换为基于卷积的类别证据层，SoftCAM保持空间信息，生成明确的类别激活图，作为预测依据。在三个医学数据集上的评估表明，SoftCAM在维持分类性能的同时，显著提升解释的定性和定量质量，优于现有事后方法。结果证明，CNN可在不牺牲性能的前提下实现内在可解释性，推动高风险决策中自解释深度学习的发展。代码已公开于https://github.com/kdjoumessi/SoftCAM。
原文摘要 · Abstract (English)
Convolutional neural networks (CNNs) are widely used for high-stakes applications like medicine, often surpassing human performance. However, most explanation methods rely on post-hoc attribution, approximating the decision-making process of already trained black-box models. These methods are often sensitive, unreliable, and fail to reflect true model reasoning, limiting their trustworthiness in critical applications. In this work, we introduce SoftCAM, a straightforward yet effective approach that makes standard CNN architectures inherently interpretable. By removing the global average pooling layer and replacing the fully connected classification layer with a convolution-based class evidence layer, SoftCAM preserves spatial information and produces explicit class activation maps that form the basis of the model's predictions. Evaluated on three medical datasets, SoftCAM maintains classification performance while significantly improving both the qualitative and quantitative explanation compared to existing post-hoc methods. Our results demonstrate that CNNs can be inherently interpretable without compromising performance, advancing the development of self-explainable deep learning for high-stakes decision-making. The code is available at https://github.com/kdjoumessi/SoftCAM
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