Effortless Vision-Language Model Specialization in Histopathology without Annotation

• URL: http://arxiv.org/abs/2508.07835v1
• Date: Mon, 11 Aug 2025 10:39:27 GMT
• Title: Effortless Vision-Language Model Specialization in Histopathology without Annotation
• Authors: Jingna Qiu, Nishanth Jain, Jonas Ammeling, Marc Aubreville, Katharina Breininger,
• Abstract summary: Vision-Language Models (VLMs) have demonstrated impressive zero-shot classification capabilities across various tasks.<n>Their general-purpose design may lead to suboptimal performance in specific downstream applications.<n>This paper investigates annotation-free adaptation of VLMs through continued pretraining on domain- and task-relevant image-caption pairs.
• Score: 0.4154350202907906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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