  Deep learning models achieve strong performance for radiology image
classification, but their practical application is bottlenecked by the need for
large labeled training datasets. Semi-supervised learning (SSL) approaches
leverage small labeled datasets alongside larger unlabeled datasets and offer
potential for reducing labeling cost. In this work, we introduce NoTeacher, a
novel consistency-based SSL framework which incorporates probabilistic
graphical models. Unlike Mean Teacher which maintains a teacher network updated
via a temporal ensemble, NoTeacher employs two independent networks, thereby
eliminating the need for a teacher network. We demonstrate how NoTeacher can be
customized to handle a range of challenges in radiology image classification.
Specifically, we describe adaptations for scenarios with 2D and 3D inputs, uni
and multi-label classification, and class distribution mismatch between labeled
and unlabeled portions of the training data. In realistic empirical evaluations
on three public benchmark datasets spanning the workhorse modalities of
radiology (X-Ray, CT, MRI), we show that NoTeacher achieves over 90-95% of the
fully supervised AUROC with less than 5-15% labeling budget. Further, NoTeacher
outperforms established SSL methods with minimal hyperparameter tuning, and has
implications as a principled and practical option for semisupervised learning
in radiology applications.
