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arXiv · 2508.07835

Effortless Vision-Language Model Specialization in Histopathology without Annotation

Jingna Qiu·Nishanth Jain·Jonas Ammeling·Marc Aubreville·Katharina Breininger
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Abstract

Recent advances in Vision-Language Models (VLMs) in histopathology, such as CONCH and QuiltNet, have demonstrated impressive zero-shot classification capabilities across various tasks. However, their general-purpose design may lead to suboptimal performance in specific downstream applications. While supervised fine-tuning methods address this issue, they require manually labeled samples for adaptation. This paper investigates annotation-free adaptation of VLMs through continued pretraining on domain- and task-relevant image-caption pairs extracted from existing databases. Our experiments on two VLMs, CONCH and QuiltNet, across three downstream tasks reveal that these pairs substantially enhance both zero-shot and few-shot performance. Notably, with larger training sizes, continued pretraining matches the performance of few-shot methods while eliminating manual labeling. Its effectiveness, task-agnostic design, and annotation-free workflow make it a promising pathway for adapting VLMs to new histopathology tasks. Code is available at https://github.com/DeepMicroscopy/Annotation-free-VLM-specialization.

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Jingna Qiu, Nishanth Jain, Jonas Ammeling, Marc Aubreville, Katharina Breininger. 2025-08-11. Effortless Vision-Language Model Specialization in Histopathology without Annotation. https://arxiv.org/abs/2508.07835

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