Book — 1 online resource (xiii, 280 pages) : illustrations Digital: text file.PDF.
Domain adaptation and transfer learning
Crowd-sourcing annotations and fusion of labels from different sources
Modelling of label uncertainty
Visualization and human-computer interaction
Medical imaging-based diagnosis
Medical signal-based diagnosis
Medical image reconstruction and model selection using deep learning techniques
Meta-heuristic techniques for fine-tuning
Parameter in deep learning-based architectures
Applications based on deep learning techniques.
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty. The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.