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We present a novel technique to discover and exploit weak causal signalsdirectly from images via neural networks for classification purposes. This way,we model how the presence of a feature in one part of the image affects theappearance of another feature in a different part of the image. Our methodconsists of a convolutional neural network backbone and a causality-factorsextractor module, which computes weights to enhance each feature map accordingto its causal influence in the scene. We develop different architecturevariants and empirically evaluate all the models on two public datasets ofprostate MRI images and breast histopathology slides for cancer diagnosis. Westudy the effectiveness of our module both in fully-supervised and few-shotlearning, we assess its addition to existing attention-based solutions, weconduct ablation studies, and investigate the explainability of our models viaclass activation maps. Our findings show that our lightweight block extractsmeaningful information and improves the overall classification, together withproducing more robust predictions that focus on relevant parts of the image.That is crucial in medical imaging, where accurate and reliable classificationsare essential for effective diagnosis and treatment planning.

Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results