Clinical-grade Multi-Organ Pathology Report Generation for Multi-scale Whole Slide Images via a Semantically Guided Medical Text Foundation Model
<|reference_start|>Clinical-grade Multi-Organ Pathology Report Generation for Multi-scale Whole Slide Images via a Semantically Guided Medical Text Foundation Model: Vision language models (VLM) have achieved success in both natural language comprehension and image recognition tasks. However, their use in pathology report generation for whole slide images (WSIs) is still limited due to the huge size of multi-scale WSIs and the high cost of WSI annotation. Moreover, in most of the existing research on pathology report generation, sufficient validation regarding clinical efficacy has not been conducted. Herein, we propose a novel Patient-level Multi-organ Pathology Report Generation (PMPRG) model, which utilizes the multi-scale WSI features from our proposed multi-scale regional vision transformer (MR-ViT) model and their real pathology reports to guide VLM training for accurate pathology report generation. The model then automatically generates a report based on the provided key features attended regional features. We assessed our model using a WSI dataset consisting of multiple organs, including the colon and kidney. Our model achieved a METEOR score of 0.68, demonstrating the effectiveness of our approach. This model allows pathologists to efficiently generate pathology reports for patients, regardless of the number of WSIs involved.<|reference_end|>
@article{tan2024clinical-grade,
    title={Clinical-grade Multi-Organ Pathology Report Generation for Multi-scale
  Whole Slide Images via a Semantically Guided Medical Text Foundation Model},
    author={Jing Wei Tan, SeungKyu Kim, Eunsu Kim, Sung Hak Lee, Sangjeong Ahn,
  Won-Ki Jeong},
    journal={arXiv preprint arXiv:2409.15574},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15574},
    primaryClass={cs.CV}
}