ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?

Doanh C. Bui; Hoai Luan Pham; Vu Trung Duong Le; Tuan Hai Vu; Van Duy Tran; Yasuhiko Nakashima

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arXiv · 2504.15627

ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?

Doanh C. Bui·Hoai Luan Pham·Vu Trung Duong Le·Tuan Hai Vu·Van Duy Tran·Yasuhiko Nakashima

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Abstract

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.

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Doanh C. Bui, Hoai Luan Pham, Vu Trung Duong Le, Tuan Hai Vu, Van Duy Tran, Yasuhiko Nakashima. 2025-04-22. ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?. https://arxiv.org/abs/2504.15627

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