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.

翻译：深度神经网络在癌症辅助诊断等关键应用中正逐步展现其价值。然而，要让其输出结果在实践中被接受，所提供的结论必须以病理学家易于理解的方式进行解释。遮挡法是一种广为人知且广泛使用的解释技术，但其计算过程可能耗时较长，从而延缓了与病理学家的协作开发进程。本研究旨在为一项成功的前列腺癌检测系统寻找更快速的遮挡法替代方案。由于目前尚无成熟的框架用于比较不同解释方法的性能，我们首先确定了合适的比较标准并选取了相应的评估指标。基于实验结果，我们成功选择了一种不同的解释方法，在保证输出质量不受负面影响的前提下，将原先所需的解释时间缩短了至少10倍。这种加速效果使得模型开发与调试能够快速迭代，并推动人工智能辅助前列腺癌检测向临床实际应用更近一步。我们提出，本研究中寻找遮挡法替代方案的研究思路，可推广至其他相关应用中候选方法的评估。