  The detection of brain metastases (BM) in their early stages could have a
positive impact on the outcome of cancer patients. We previously developed a
framework for detecting small BM (with diameters of less than 15mm) in
T1-weighted Contrast-Enhanced 3D Magnetic Resonance images (T1c) to assist
medical experts in this time-sensitive and high-stakes task. The framework
utilizes a dedicated convolutional neural network (CNN) trained using labeled
T1c data, where the ground truth BM segmentations were provided by a
radiologist. This study aims to advance the framework with a noisy
student-based self-training strategy to make use of a large corpus of unlabeled
T1c data (i.e., data without BM segmentations or detections). Accordingly, the
work (1) describes the student and teacher CNN architectures, (2) presents data
and model noising mechanisms, and (3) introduces a novel pseudo-labeling
strategy factoring in the learned BM detection sensitivity of the framework.
Finally, it describes a semi-supervised learning strategy utilizing these
components. We performed the validation using 217 labeled and 1247 unlabeled
T1c exams via 2-fold cross-validation. The framework utilizing only the labeled
exams produced 9.23 false positives for 90% BM detection sensitivity; whereas,
the framework using the introduced learning strategy led to ~9% reduction in
false detections (i.e., 8.44) for the same sensitivity level. Furthermore,
while experiments utilizing 75% and 50% of the labeled datasets resulted in
algorithm performance degradation (12.19 and 13.89 false positives
respectively), the impact was less pronounced with the noisy student-based
training strategy (10.79 and 12.37 false positives respectively).
