PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology

TL;DR

PathFLIP introduces a fine-grained, region-level language-image pretraining framework for whole slide images in pathology, enabling precise interpretation, localization, and versatile clinical task performance with less data.

Contribution

It presents a novel approach that decomposes slide captions into subcaptions and generates region-specific embeddings, improving fine-grained understanding and adaptability in computational pathology.

Findings

Outperforms existing models on four benchmarks.

Requires less training data than prior methods.

Excels in diverse tasks like classification, retrieval, and localization.

Abstract

While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide Images (WSIs) continue to pose challenges for multimodal understanding. Existing alignment methods struggle to capture fine-grained correspondences between textual descriptions and visual cues across thousands of patches from a slide, compromising their performance on downstream tasks. In this paper, we propose PathFLIP (Pathology Fine-grained Language-Image Pretraining), a novel framework for holistic WSI interpretation. PathFLIP decomposes slide-level captions into region-level subcaptions and generates text-conditioned region embeddings to facilitate precise visual-language grounding. By harnessing Large Language Models (LLMs), PathFLIP can seamlessly follow diverse clinical instructions and adapt to varied diagnostic contexts. Furthermore, it exhibits versatile capabilities across multiple paradigms, efficiently handling slide-level classification and retrieval, fine-grained lesion localization, and instruction following. Extensive experiments demonstrate that PathFLIP outperforms existing large-scale pathological VLMs on four representative benchmarks while requiring significantly less training data, paving the way for fine-grained, instruction-aware WSI interpretation in clinical practice.