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Explainable deep neural networks for medical image analysis

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Although deep neural networks have already achieved a good performance in many medical image analysis tasks, their clinical implementation is slower than many anticipated a few years ago. One of the critical issues that remains outstanding is the lack of explainability of the commonly used network architectures imported from computer vision. In my talk, I will explain how we created a new deep neural network architecture, tailored to medical image analysis, in which making a prediction is inseparable from explaining it. I will demonstrate how we used this architecture to build strong networks for breast cancer screening exam interpretation and COVID -19 prognosis.

This talk is part of the CuAI (Cambridge University Artificial Intelligence Society) series.

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