PathFLIP

Model weights for the paper PathFLIP: Fine-Grained Language-Image Pretraining for Versatile Pathology Image Understanding.

Overview

PathFLIP is a pathology vision-language model that aligns fine-grained morphological sub-captions with their corresponding regions in Whole Slide Images. Unlike prior pathology VLMs that pair an entire slide with a single report-level anchor, PathFLIP introduces region-statement correspondence through a region Q-Former and a region-level contrastive objective with caption-swapped negatives, learning region-level alignment without any manual spatial annotation. This fine-grained supervision enables strong slide-level classification and retrieval performance, and gives rise to an emergent visual grounding capability.

Model Details

Base model: Qwen3-0.6B
Training data: FGC-4K Dataset
Task: classification, image-text retrieval, visual grounding, vqa
Languages: English

License

This model is released under CC BY-NC 4.0 — free for academic and research use, not for commercial use or clinical deployment.

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Model size

0.1B params

Tensor type

F32

BF16