| # PA-LLaVA-plus | |
| This is the first-stage weights trained on the 400w pathological image-text dataset using the PA-LLaVA model structure. The link is https://huggingface.co/OpenFace-CQUPT/PA-LLaVA-plus. | |
| ## 400w Dataset | |
| The 400w pathology dataset is derived from the publicly available "Accessible Dataset (18M samples)" from MedTrinity-25M([UCSC-VLAA/MedTrinity-25M · Datasets at Hugging Face](https://huggingface.co/datasets/UCSC-VLAA/MedTrinity-25M)). This is a dataset spanning multiple medical fields. By analyzing the linguistic structure of the text in this dataset, we extracted a 400w dataset specific to the pathology domain. | |
|  | |
| ## Citation | |
| ``` | |
| @INPROCEEDINGS{10821785, | |
| author={Dai, Dawei and Zhang, Yuanhui and Xu, Long and Yang, Qianlan and Shen, Xiaojing and Xia, Shuyin and Wang, Guoyin}, | |
| booktitle={2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, | |
| title={PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding}, | |
| year={2024}, | |
| volume={}, | |
| number={}, | |
| pages={3138-3143}, | |
| keywords={Connectors;Pathology;Visualization;Codes;Computational modeling;Biological system modeling;Data models;Cleaning;Bioinformatics;Biomedical imaging;Pathology Image Understanding;VQA;LLaVA}, | |
| doi={10.1109/BIBM62325.2024.10821785}} | |
| @article{dai2025pathologyvlm, | |
| title={Pathologyvlm: a large vision-language model for pathology image understanding}, | |
| author={Dai, Dawei and Zhang, Yuanhui and Yang, Qianlan and Xu, Long and Shen, Xiaojing and Xia, Shuyin and Wang, Guoyin}, | |
| journal={Artificial Intelligence Review}, | |
| volume={58}, | |
| number={6}, | |
| pages={1--19}, | |
| year={2025}, | |
| publisher={Springer} | |
| } | |
| ``` | |
| --- | |
| license: cc | |
| --- | |