  Gliomas are the most frequent primary brain tumors in adults. Glioma change
detection aims at finding the relevant parts of the image that change over
time. Although Deep Learning (DL) shows promising performances in similar
change detection tasks, the creation of large annotated datasets represents a
major bottleneck for supervised DL applications 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 our source weak labels are generated automatically from radiology
reports via NLP. 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 ResNeXt model. We evaluate our models
on 1693 T2-weighted magnetic resonance imaging difference maps created from 183
patients, by classifying them into stable or unstable according to tumor
evolution. The weak labels extracted from radiology reports allowed us to
increase dataset size more than 3-fold, and improve VGG classification results
from 75% to 82% AUC. Mixed training from scratch led to higher performance than
fine-tuning or feature extraction. To assess generalizability, we 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
datasets, and that report-generated weak labels are effective in improving
model performances. Code, in-house dataset and BraTS labels are released.
