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

XDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System In Colorectal Cancer