自适应原型基于的前列腺癌可解释分级
Adaptive Prototype-based Interpretable Grading of Prostate Cancer
AI总结：
本文提出了一种基于原型的弱监督框架，用于从组织病理学图像中可解释地对前列腺癌进行分级，通过预训练、原型感知损失函数和动态剪枝机制提升可解释性和诊断准确性。
摘要：
前列腺癌是男性中经常诊断出的恶性肿瘤之一，日益增长的活检需求给病理科医生带来了沉重的工作量。分级过程繁琐且主观，推动了自动化系统的开发。尽管深度学习在性能方面取得了进展，但其有限的可解释性对在医疗等高风险应用中的广泛应用构成了挑战。现有的前列腺癌分类器可解释性技术提供了粗略的解释，但并未揭示为什么突出区域重要。在此情况下，我们提出了一种新的基于原型的弱监督框架，用于从组织病理学图像中可解释地对前列腺癌进行分级。这些网络可以更加可信，因为其显式推理过程与病理科医生在比较可疑区域与临床验证示例时的工作流程相似。网络最初在块级别上预训练，以学习与每个等级相关的鲁棒原型特征。为了适应弱监督设置下的前列腺癌分级，网络使用新的原型感知损失函数进行微调。最后，引入了一种基于注意力的动态剪枝机制来处理样本间异质性，同时选择性地强调相关原型以获得最佳性能。在基准PANDA和SICAP数据集上的广泛验证证实，该框架可以作为病理科医生在日常诊断工作流程中的可靠辅助工具。
英文摘要：
Prostate cancer being one of the frequently diagnosed malignancy in men, the rising demand for biopsies places a severe workload on pathologists. The grading procedure is tedious and subjective, motivating the development of automated systems. Although deep learning has made inroads in terms of performance, its limited interpretability poses challenges for widespread adoption in high-stake applications like medicine. Existing interpretability techniques for prostate cancer classifiers provide a coarse explanation but do not reveal why the highlighted regions matter. In this scenario, we propose a novel prototype-based weakly-supervised framework for an interpretable grading of prostate cancer from histopathology images. These networks can prove to be more trustworthy since their explicit reasoning procedure mirrors the workflow of a pathologist in comparing suspicious regions with clinically validated examples. The network is initially pre-trained at patch-level to learn robust prototypical features associated with each grade. In order to adapt it to a weakly-supervised setup for prostate cancer grading, the network is fine-tuned with a new prototype-aware loss function. Finally, a new attention-based dynamic pruning mechanism is introduced to handle inter-sample heterogeneity, while selectively emphasizing relevant prototypes for optimal performance. Extensive validation on the benchmark PANDA and SICAP datasets confirms that the framework can serve as a reliable assistive tool for pathologists in their routine diagnostic workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
