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

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Saeid Asgari Taghanaki,Arkadeep Das,Ghassan Hamarnehing & analysis, liquid biopsies, and more.Relates cancer gen.This popular textbook, now in its third edition, provides a theoretical framework for understanding why cancers arise, how they develop and how they can be treated..Particular attention is devoted to the origins of cancer and the applicati

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Sérgio Pereira,Raphael Meier,Victor Alves,Mauricio Reyes,Carlos A. Silvao treat patients with cancer, which includes Surgery, Radiation therapy, Chemotherapy, Targeted therapy and Immunotherapy. The efficiency of all these treatments is limited by the capacity of cancer cells to escape therapy. This book explains the mechanisms of anti-cancer drug resistance and strateg

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Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Datadology to measure the impact of batch effects in classification studies and propose a technique for solving batch effects under the assumption that they are caused by a linear transformation. We empirically show that this approach consistently improve the performance of classifiers in multi-site sce

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To Learn or Not to Learn Features for Deformable Registration?ld for low level features. This shows that when handcrafted features are designed based on good insights into the problem at hand, they perform better or are comparable to features learnt using deep learning framework.