Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization

Understand

Adopting Convolutional Neural Networks (CNNs) in the daily routine of primary diagnosis requires not only near-perfect precision, but also a sufficient degree of generalization to data acquisition shifts and transparency.

• Existing CNN models act as black boxes, not ensuring to the physicians that important diagnostic features are used by the model.
• Building on top of successfully existing techniques such as multi-task learning, domain adversarial training and concept-based interpretability, this paper addresses the challenge of introducing diagnostic factors in the training objectives.
• Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e.g.