Computer Vision News - March 2022

36 Congrats, Doctor! when a model is useful for the clinical application. In this field, mistakes come at a high cost, and understanding the limitations of a model is fundamental. If pitfalls are uncovered, new models can be built to be more reliable and trustworthy than the already existing ones. Being the end-users of our developments, physicians should be part of the model evaluation process, though they often ignore the processes of feature extraction, model selection and training. If they were explained which features are used by the model to obtain a certain output, they would then be able to predict eventual failures and unexpected outcomes . So can we make Deep Learning models for medical image classification more understandable to physicians? How can this analysis be used to improve model generalization? Physician-friendly Explanations We proposed visual and concept-based explanations that are understandable by pathologists, focusing on the application of breast cancer detection in digital scans of tissue samples. Our work on Sharp- LIME uses nuclei contours to generate neat heatmaps that highlight the relevance of individual nuclei to the prediction. With Regression Concept Vectors I have generated explanations in terms of nuclei morphology and appearance. These works improved the understandability of the explanations by physicians and generated new insights on the CNN mechanics for cancer detection. Guiding CNNs to learn interpretable features CNNs were shown to be biased towards The models that we work with in machine learning are approximations based on assumptions that are never exactly true. Even if wrong, some models may be useful. I find it rather compelling to understand Mara Graziani recently completed her Ph.D. at the University of Geneva and the University of Applied Sciences of Western Switzerland (Hes-so Valais). She researches interpretability techniques for deep learning models used in healthcare. She will now embrace a new postdoctoral adventure at IBM Research Zurich and the Zurich University of Applied Sciences (ZHAW) trying to apply interpretability on systems biology. Congrats, Doctor Mara!