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license: apache-2.0 |
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language: |
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- zh |
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- en |
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# PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding |
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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. |
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##### Our code is publicly available on Github.[ddw2AIGROUP2CQUPT/PA-LLaVA (github.com)](https://github.com/ddw2AIGROUP2CQUPT/PA-LLaVA) |
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## Architecture |
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## Data Cleaning Process |
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Only the image names of the cleaned dataset are provided here, for the specific training code please visit our Github. |
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## contact |
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mailto: [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]) |
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