Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts

Jin Li; Deepta Rajan; Chintan Shah; Dinkar Juyal; Shreya Chakraborty; Chandan Akiti; Filip Kos; Janani Iyer; Anand Sampat; Ali Behrooz

arXiv · 2211.07692

Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts

Jin Li·Deepta Rajan·Chintan Shah·Dinkar Juyal·Shreya Chakraborty·Chandan Akiti·Filip Kos·Janani Iyer·Anand Sampat·Ali Behrooz

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Abstract

Histopathology images are gigapixel-sized and include features and information at different resolutions. Collecting annotations in histopathology requires highly specialized pathologists, making it expensive and time-consuming. Self-training can alleviate annotation constraints by learning from both labeled and unlabeled data, reducing the amount of annotations required from pathologists. We study the design of teacher-student self-training systems for Non-alcoholic Steatohepatitis (NASH) using clinical histopathology datasets with limited annotations. We evaluate the models on in-distribution and out-of-distribution test data under clinical data shifts. We demonstrate that through self-training, the best student model statistically outperforms the teacher with a $3\%$ absolute difference on the macro F1 score. The best student model also approaches the performance of a fully supervised model trained with twice as many annotations.

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Jin Li, Deepta Rajan, Chintan Shah, Dinkar Juyal, Shreya Chakraborty, Chandan Akiti, Filip Kos, Janani Iyer, Anand Sampat, Ali Behrooz. 2022-11-14. Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts. https://arxiv.org/abs/2211.07692

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