Prompting Whole Slide Image Based Genetic Biomarker Prediction
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911955444301824 |
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| author | Zhang, Ling Yun, Boxiang Xie, Xingran Li, Qingli Li, Xinxing Wang, Yan |
| author_facet | Zhang, Ling Yun, Boxiang Xie, Xingran Li, Qingli Li, Xinxing Wang, Yan |
| contents | Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this paper, we propose a whole slide image (WSI) based genetic biomarker prediction method via prompting techniques. Our work aims at addressing the following challenges: (1) extracting foreground instances related to genetic biomarkers from gigapixel WSIs, and (2) the interaction among the fine-grained pathological components in WSIs.Specifically, we leverage large language models to generate medical prompts that serve as prior knowledge in extracting instances associated with genetic biomarkers. We adopt a coarse-to-fine approach to mine biomarker information within the tumor microenvironment. This involves extracting instances related to genetic biomarkers using coarse medical prior knowledge, grouping pathology instances into fine-grained pathological components and mining their interactions. Experimental results on two colorectal cancer datasets show the superiority of our method, achieving 91.49% in AUC for MSI classification. The analysis further shows the clinical interpretability of our method. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/PromptBio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09540 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompting Whole Slide Image Based Genetic Biomarker Prediction Zhang, Ling Yun, Boxiang Xie, Xingran Li, Qingli Li, Xinxing Wang, Yan Image and Video Processing Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning Tissues and Organs Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this paper, we propose a whole slide image (WSI) based genetic biomarker prediction method via prompting techniques. Our work aims at addressing the following challenges: (1) extracting foreground instances related to genetic biomarkers from gigapixel WSIs, and (2) the interaction among the fine-grained pathological components in WSIs.Specifically, we leverage large language models to generate medical prompts that serve as prior knowledge in extracting instances associated with genetic biomarkers. We adopt a coarse-to-fine approach to mine biomarker information within the tumor microenvironment. This involves extracting instances related to genetic biomarkers using coarse medical prior knowledge, grouping pathology instances into fine-grained pathological components and mining their interactions. Experimental results on two colorectal cancer datasets show the superiority of our method, achieving 91.49% in AUC for MSI classification. The analysis further shows the clinical interpretability of our method. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/PromptBio. |
| title | Prompting Whole Slide Image Based Genetic Biomarker Prediction |
| topic | Image and Video Processing Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning Tissues and Organs |
| url | https://arxiv.org/abs/2407.09540 |