ZeroSlide：在病理学视觉-语言基础模型时代，零样本分类是否足以胜任全切片图像分析中的终身学习？
ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?
AI总结：
本研究首次比较传统持续学习方法与视觉-语言零样本分类在全切片图像终身学习中的表现，探讨基础模型是否足以应对多任务持续学习挑战。
摘要：
全切片图像（WSIs）的终身学习面临的挑战是，以分布式、持续的方式训练一个统一模型来执行多项与WSI相关的任务，如癌症亚型分类和肿瘤分类。这在临床和医院中是一个实际且适用的问题，因为WSIs体积大，需要存储、处理和传输时间。每当定义新任务时就训练新模型非常耗时。近期工作已将基于正则化和基于重放的方法应用于此场景。然而，将诊断文本与病理图像对齐的视觉-语言基础模型的兴起提出了一个问题：仅凭这些模型，使用零样本分类是否足以胜任WSI的终身学习，还是需要进一步研究持续学习策略以提升性能？据我们所知，这是首个将传统持续学习方法与视觉-语言零样本分类用于WSI进行比较的研究。我们的源代码和实验结果将很快公开。
英文摘要：
Lifelong learning for whole slide images (WSIs) poses the challenge of training a unified model to perform multiple WSI-related tasks, such as cancer subtyping and tumor classification, in a distributed, continual fashion. This is a practical and applicable problem in clinics and hospitals, as WSIs are large, require storage, processing, and transfer time. Training new models whenever new tasks are defined is time-consuming. Recent work has applied regularization- and rehearsal-based methods to this setting. However, the rise of vision-language foundation models that align diagnostic text with pathology images raises the question: are these models alone sufficient for lifelong WSI learning using zero-shot classification, or is further investigation into continual learning strategies needed to improve performance? To our knowledge, this is the first study to compare conventional continual-learning approaches with vision-language zero-shot classification for WSIs. Our source code and experimental results will be available soon.
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。
