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PathBLIP 2

A vision-language model built upon the BLIP-2 framework using BioGPT and HIPT for pathology report generation and cross-modal retrieval of melanocytic lesions.

KindOtherVersionRevision 8c81fe9

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Kind
Other
File size
1.54–2.06 GB
Version
vRevision 8c81fe9
Updated
Sep 17, 2026
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0
Tasks
Captioning
Source
Hugging Face

Source description

Model Card for PathBLIP-2

A vision-language model built upon the BLIP-2 framework using BioGPT and HIPT for pathology report generation and cross-modal retrieval of melanocytic lesions.

Model Details

This repository contains multiple checkpoints for the model which was used for the experiments in the paper. The model was trained and evaluated on a dataset of 19,636 melanocytic lesion cases, consisting of one or more whole slide images (WSIs) and a pathology report, using different training configurations. The supporting code is available from the corresponding GitHub repository. We refer to the paper for more information regarding the dataset, training, evaluation, and limitations.

Sources

1 source
Hugging FacePrimaryVersion 8c81fe9aa66895789b9e3247c5c7a1560dc27fb2
huggingface.co/RTLucassen/PathBLIP-2/tree/8c81fe9aa66895789b9e3247c5c7a1560dc27fb2

Verified Sep 17

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Model artifacts

4 artifacts

stage_1_pretrained_stage_2/checkpoint.pth

pth · 2.06 GB · SHA-256 503e703daeb9…a6b1 · Hugging Face

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stage_1/checkpoint.pth

pth · 2.06 GB · SHA-256 21814abd04b4…f07f · Hugging Face

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stage_2_full_report/checkpoint.pth

pth · 1.54 GB · SHA-256 0d68f48e0747…74f4 · Hugging Face

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stage_2_he_only/checkpoint.pth

pth · 1.54 GB · SHA-256 a8ba4d70b47d…bd0d · Hugging Face

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Source excerpts

2 excerpts

--- license: apache-2.0 language: - en pipeline_tag: image-to-text tags: - medical --- # Model Card for PathBLIP-2 A vision-language model built upon the BLIP-2 framework using BioGPT and HIPT for pathology report generation and cross-modal retrieval of melanocytic lesions. ## Model Details This repository contains multiple checkpoints for the model which was used for the experiments in the paper. The model was trained and evaluated on a dataset of 19,636 melanocytic lesion cases, consisting of one or more whole slide images (WSIs) and a pathology report, using different training configurations. The supporting code is available from the corresponding GitHub repository. We refer to the paper for more information regarding the dataset, training, evaluation, and limitations. - **Paper: *["On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation"](https://openreview.net/forum?id=5fQchwJgQr&referrer=%5Bthe%20profile%20of%20Ruben%20Lucassen%5D(%2Fprofile%3Fid%3D~Ruben_Lucassen1))*** - **Repository:** [GitHub](https://github.com/nuldertien/PathBLIP-2) - **Framework:** [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2) - **Base language model:** [BioGPT](https://huggingface.co/microsoft/biogpt) - **WSI feature extractor:** [HIPT](https://github.com/mahmoodlab/HIPT) - **License:** Apache-2.0

Source context: 0 downloads · 1 likes · Pipeline image-to-text · Repo RTLucassen/PathBLIP-2