改进医学影像解释的清晰度，让AI诊断结果更可信。
Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks
- 用新方法替代传统线性层，生成可直接解读的诊断依据。
- 在胸片数据集上保持高准确率，且解释图无模糊伪影。
- 适合临床医生信任和使用，尤其多病种多标签场景。
深度神经网络在医疗影像等安全关键领域应用时，可解释性与忠实性至关重要。B-cos网络通过权重-输入对齐机制替代标准线性层，能生成无需后处理的类特定解释。然而，标准B-cos模型在解释图中存在严重混叠伪影，影响临床可用性。本文提出引入FLCPooling（FLC）和BlurPool（BP）两种抗混叠策略，显著提升解释质量。在多个胸片数据集上的实验表明，改进后的$ ext{B-cos}_ ext{FLC}$与$ ext{B-cos}_ ext{BP}$在保持与顶尖DNN相当诊断性能的同时，提供忠实且无伪影的解释，适用于多分类与多标签临床场景。代码已开源。
原文摘要 · Abstract (English)
Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical imaging. B-cos networks offer a promising solution by replacing standard linear layers with a weight-input alignment mechanism, producing inherently interpretable, class-specific explanations without post-hoc methods. While maintaining diagnostic performance competitive with state-of-the-art DNNs, standard B-cos models suffer from severe aliasing artifacts in their explanation maps, making them unsuitable for clinical use where clarity is essential. In this work, we address these limitations by introducing anti-aliasing strategies using FLCPooling (FLC) and BlurPool (BP) to significantly improve explanation quality. Our experiments on chest X-ray datasets demonstrate that the modified $\text{B-cos}_\text{FLC}$ and $\text{B-cos}_\text{BP}$ preserve strong predictive performance while providing faithful and artifact-free explanations suitable for clinical application in multi-class and multi-label settings. Code available at: GitHub repository (url: https://github.com/mkleinma/B-cos-medical-paper).
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