license: apache-2.0
language:
- zh
- en
PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding
We developed a domain-speciffc large language-vision assistant (PA-LLaVA) for pathology image understanding. Specifically, (1) we first construct a human pathology image-text dataset by cleaning the public medical image-text data for domainspecific alignment; (2) Using the proposed image-text data, we first train a pathology language-image pretraining (PLIP) model as the specialized visual encoder for pathology image, and then we developed scale-invariant connector to avoid the information loss caused by image scaling; (3) We adopt two-stage learning to train PA-LLaVA, first stage for domain alignment, and second stage for end to end visual question & answering (VQA) task.
Our code is publicly available on Github.ddw2AIGROUP2CQUPT/PA-LLaVA (github.com)
Architecture
Data Cleaning Process
Only the image names of the cleaned dataset are provided here, for the specific training code please visit our Github.
contact
mailto: [email protected] or [email protected]