  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.

Computer Vision and Pattern Recognition