基于字典的病理挖掘与硬实例辅助分类器去偏方法用于从组织切片中预测遗传生物标志物
Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction from WSIs
AI总结：
本文提出D2Bio框架，通过字典-based层级病理挖掘和硬实例辅助分类器去偏方法，解决病理表示构建和WSI中无关区域导致的过拟合问题，实验显示在五个队列中AUROC提升超4%。
摘要：
遗传生物标志物预测，如结直肠癌中的微卫星不稳定性，对临床决策至关重要。但两个主要挑战阻碍了准确预测：(1) 构建包含病理成分复杂相互关系的病理感知表示困难。(2) WSIs包含大量与遗传生物标志物无关的区域，使模型容易过拟合简单但无关的实例。本文提出基于字典的层级病理挖掘与硬实例辅助分类器去偏框架D2Bio，以解决这些问题。我们的第一个模块，基于字典的层级病理挖掘，能够挖掘多样且非常细粒度的病理上下文交互，无需限制patch间的距离。第二个模块，硬实例辅助分类器去偏，通过聚焦于困难但任务相关的特征学习去偏分类器，无需额外标注。在五个队列上的实验结果表明，我们的方法优于第二名，TCGA-CRC-MSI队列中AUROC提升超过4%。进一步分析显示D2Bio在遗传生物标志物诊断中的临床可解释性和在生存分析中的潜在临床应用。代码将在https://github.com/DeepMed-Lab-ECNU/D2Bio上提供。
英文摘要：
Prediction of genetic biomarkers, e.g., microsatellite instability in colorectal cancer is crucial for clinical decision making. But, two primary challenges hamper accurate prediction: (1) It is difficult to construct a pathology-aware representation involving the complex interconnections among pathological components. (2) WSIs contain a large proportion of areas unrelated to genetic biomarkers, which make the model easily overfit simple but irrelative 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 classfier 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 will be available at https://github.com/DeepMed-Lab-ECNU/D2Bio.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
