用更快方法替代传统解释技术，让癌症诊断AI更实时可信。
Beyond Occlusion: In Search for Near Real-Time Explainability of CNN-Based Prostate Cancer Classification
- 设计新解释方法，计算速度比原方案快10倍以上
- 保持解释质量不变，适用于病理医生快速审阅
- 适合临床场景下需快速反馈的AI辅助诊断系统
深度神经网络在辅助癌症诊断等关键应用中逐渐展现价值。然而，其输出要被病理医生采纳，必须具备可解释性。目前广泛使用的遮挡法（occlusion）计算耗时长，限制了交互效率。本文针对前列腺癌检测系统，探索更快的解释方法替代方案。由于缺乏统一评估框架，我们先定义了合适的评价标准并选取对应指标。基于评估结果，选择了一种新方法，使解释时间至少降低一个数量级，且未影响输出质量。该加速显著提升了模型开发与调试的迭代速度，推动AI辅助诊断在临床中的落地。我们提出的方法可推广至其他类似应用场景。
原文摘要 · Abstract (English)
Deep neural networks are starting to show their worth in critical applications such as assisted cancer diagnosis. However, for their outputs to get accepted in practice, the results they provide should be explainable in a way easily understood by pathologists. A well-known and widely used explanation technique is occlusion, which, however, can take a long time to compute, thus slowing the development and interaction with pathologists. In this work, we set out to find a faster replacement for occlusion in a successful system for detecting prostate cancer. Since there is no established framework for comparing the performance of various explanation methods, we first identified suitable comparison criteria and selected corresponding metrics. Based on the results, we were able to choose a different explanation method, which cut the previously required explanation time at least by a factor of 10, without any negative impact on the quality of outputs. This speedup enables rapid iteration in model development and debugging and brings us closer to adopting AI-assisted prostate cancer detection in clinical settings. We propose that our approach to finding the replacement for occlusion can be used to evaluate candidate methods in other related applications.
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