Titlebook: Understanding and Interpreting Machine Learning in Medical Image Computing Applications; First International Danail Stoyanov,Zeike Taylor,

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Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classificationowever, as neural networks are black box function approximators, it is difficult, if not impossible, for a medical expert to reason about their output. This could potentially result in the expert distrusting the network when he or she does not agree with its output. In such a case, explaining why th

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Towards Complementary Explanations Using Deep Neural Networksned the attention of the scientific community due to their high accuracy in vast classification problems. However, they are still seen as black-box models where it is hard to understand the reasons for the labels that they generate. This paper proposes a deep model with monotonic constraints that ge

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How Users Perceive Content-Based Image Retrieval for Identifying Skin Imageso a specific query image. One application of CBIR in the dermatology domain is displaying a set of visually similar images with a pathology-confirmed diagnosis for a given query skin image. Recently, CBIR algorithms using machine learning with high accuracy rates have gained more attention since res

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Conference proceedings 2018 First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention,

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