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

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Exploring Adversarial Examplesd in the context of real-world images with calibrated attacks. To demystify the adversarial examples, rigorous studies need to be designed. Unfortunately, complexity of the medical images hinders such study design directly from the medical images. We hypothesize that adversarial examples might resul

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Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks through surgery. Having accurate segmentation of the ventricular system into its sub-compartments from magnetic resonance images?(MRI) would be beneficial to better characterize the condition of NPH patients. Previous segmentation algorithms need long processing time and often fail to accurately se

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Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attackses. However, there is not yet a comprehensive vulnerability analysis of these models against the so-called adversarial perturbations/attacks, which makes deep models more trustful in clinical practices. In this paper, we extensively analyzed the performance of two state-of-the-art classification dee