license: cc-by-nc-4.0
language:
- en
pipeline_tag: image-to-text
tags:
- medical
- pathology
- vision-language
- contrastive-learning
- fine-grained
- multimodal
library_name: transformers
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.