  We present a system for the prediction of microsatellite instability (MSI)
from H&E images of colorectal cancer using deep learning (DL) techniques
customized for tissue microarrays (TMAs). The system incorporates an end-to-end
image preprocessing module that produces tiles at multiple magnifications in
the regions of interest as guided by a tissue classifier module, and a
multiple-bias rejecting module. The training and validation TMA samples were
obtained from the EPICOLON project and further enriched with samples from a
single institution. A systematic study of biases at tile level identified three
protected (bias) variables associated with the learned representations of a
baseline model: the project of origin of samples, the patient spot and the TMA
glass where each spot was placed. A multiple bias rejecting technique based on
adversarial training is implemented at the DL architecture so to directly avoid
learning the batch effects of those variables. The learned features from the
bias-ablated model have maximum discriminative power with respect to the task
and minimal statistical mean dependence with the biases. The impact of
different magnifications, types of tissues and the model performance at tile vs
patient level is analyzed. The AUC at tile level, and including all three
selected tissues (tumor epithelium, mucine and lymphocytic regions) and 4
magnifications, was 0.87 +/- 0.03 and increased to 0.9 +/- 0.03 at patient
level. To the best of our knowledge, this is the first work that incorporates a
multiple bias ablation technique at the DL architecture in digital pathology,
and the first using TMAs for the MSI prediction task.
