ArXiv Preprint
Creating large annotated datasets represents a major bottleneck for the
development of deep learning models in radiology. To overcome this, we propose
a combined use of weak labels (imprecise, but fast-to-create annotations) and
Transfer Learning (TL). Specifically, we explore inductive TL, where source and
target domains are identical, but tasks are different due to a label shift: our
target labels are created manually by three radiologists, whereas the source
weak labels are generated automatically from textual radiology reports. We
frame knowledge transfer as hyperparameter optimization, thus avoiding
heuristic choices that are frequent in related works. We investigate the
relationship between model size and TL, comparing a low-capacity VGG with a
higher-capacity SEResNeXt. The task that we address is change detection in
follow-up glioma imaging: we extracted 1693 T2-weighted magnetic resonance
imaging difference maps from 183 patients, and classified them into stable or
unstable according to tumor evolution. Weak labeling allowed us to increase
dataset size more than 3-fold, and improve VGG classification results from 75%
to 82% Area Under the ROC Curve (AUC) (p=0.04). Mixed training from scratch led
to higher performance than fine-tuning or feature extraction. To assess
generalizability, we also ran inference on an open dataset (BraTS-2015: 15
patients, 51 difference maps), reaching up to 76% AUC. Overall, results suggest
that medical imaging problems may benefit from smaller models and different TL
strategies with respect to computer vision problems, and that report-generated
weak labels are effective in improving model performances. Code, in-house
dataset and BraTS labels are released.
Tommaso Di Noto, Meritxell Bach Cuadra, Chirine Atat, Eduardo Gamito Teiga, Monika Hegi, Andreas Hottinger, Patric Hagmann, Jonas Richiardi
2022-10-18
