Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts. (arXiv:2211.07692v1 [cs.CV])

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 …

arxiv machine machine learning machine learning models self-training training