ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?
On the map: Multimodal LLMs / vision-language models → Medical vision-language models
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 14 of its 20 references that are in the map. First posted on arXiv 22 Apr 2025.
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14 of 20 references.
References · 14
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- 2018Efficient Lifelong Learning with A-GEMcited by 1,058
- 2020Dark Experience for General Continual Learning: a Strong, Simple Baselinecited by 911
- 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
- 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
- 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
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- 2024Multimodal Whole Slide Foundation Model for Pathologycited by 162
- 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 134
- 2023ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysiscited by 25
- 2022AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Imagescited by 24
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- 2026Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images
- 2024Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification
- 2025MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification
- 2026Initialization matters in few-shot adaptation of vision-language models for histopathological image classification
- 2026Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols
- 2024MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological images
- 2025Investigating Zero-Shot Diagnostic Pathology in Vision-Language Models with Efficient Prompt Design
- 2025Zero-shot segmentation of skin tumors in whole-slide images with vision-language foundation models