Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction...

Prediction of genetic biomarkers, e.g., microsatellite instability in colorectal cancer is crucial for clinical decision making. However, two primary challenges hamper accurate prediction: (1) It is difficult to construct a pathology-aware representation involving the complex interconnections among pathological components. (2) Whole slide images contain a large proportion of areas unrelated to genetic biomarkers, which make the model easily overfit simple but irrelevant instances. We hereby propose a dictionary-based hierarchical pathology mining with hard-instance-assisted classifier debiasing framework to address these challenges, dubbed as D²Bio. Our first module, dictionary-based hierarchical pathology mining, is able to mine diverse and very fine-grained pathological contextual interaction without the limit to the distances between patches. The second module, hard-instance-assisted classifier debiasing, learns a debiased classifier via focusing on hard but task-related features, without any additional annotations. Experimental results on five cohorts show the superiority of our method, with over 4% improvement in AUROC compared with the second best on the TCGA-CRC-MSI cohort. Our analysis further shows the clinical interpretability of D²Bio in genetic biomarker diagnosis and potential clinical utility in survival analysis. Code, patient-level dataset splits, patch coordinates, and preprocessing scripts will be available at https://github.com/DeepMed-Lab-ECNU/D2Bio.