bibtext: @InProceedings{ Zha_Prompting_MICCAI2024,
                   author = { Zhang, Ling and Yun, Boxiang and Xie, Xingran and Li, Qingli and Li, Xinxing and Wang, Yan },
                   title = { { Prompting Whole Slide Image Based Genetic Biomarker Prediction } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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
authors: Zhang, Ling Yun, Boxiang Xie, Xingran Li, Qingli Li, Xinxing Wang, Yan