# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jshhhh/PathFLIP", dtype="auto")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.
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="jshhhh/PathFLIP")