Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings

Description

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the 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, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis. Front Matter ....Pages I-XVI Front Matter ....Pages 1-1 Alzheimer’s Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes (Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco Lorenzi, for the Alzheimer’s Disease Neuroimaging Initiative)....Pages 3-14 Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer’s Disease (Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi, for the Alzheimer’s Disease Neuroimaging Initiative)....Pages 15-23 Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease (Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter)....Pages 24-31 Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data (Roberto Vega, Russ Greiner)....Pages 32-39 Front Matter ....Pages 41-41 Towards Robust CT-Ultrasound Registration Using Deep Learning Methods (Yuanyuan Sun, Adriaan Moelker, Wiro J. Niessen, Theo van Walsum)....Pages 43-51 To Learn or Not to Learn Features for Deformable Registration? (Aabhas Majumdar, Raghav Mehta, Jayanthi Sivaswamy)....Pages 52-60 Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning (James R. Clough, Daniel R. Balfour, Claudia Prieto, Andrew J. Reader, Paul K. Marsden, Andrew P. King)....Pages 61-69 Exploring Adversarial Examples (David Kügler, Alexander Distergoft, Arjan Kuijper, Anirban Mukhopadhyay)....Pages 70-78 Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks (Muhan Shao, Shuo Han, Aaron Carass, Xiang Li, Ari M. Blitz, Jerry L. Prince et al.)....Pages 79-86 Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks (Saeid Asgari Taghanaki, Arkadeep Das, Ghassan Hamarneh)....Pages 87-94 Front Matter ....Pages 95-95 Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images (Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogerio Feris, John R. Smith)....Pages 97-105 Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment (Sérgio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, Carlos A. Silva)....Pages 106-114 Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification (Pieter Van Molle, Miguel De Strooper, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt)....Pages 115-123 Regression Concept Vectors for Bidirectional Explanations in Histopathology (Mara Graziani, Vincent Andrearczyk, Henning Müller)....Pages 124-132 Towards Complementary Explanations Using Deep Neural Networks (Wilson Silva, Kelwin Fernandes, Maria J. Cardoso, Jaime S. Cardoso)....Pages 133-140 How Users Perceive Content-Based Image Retrieval for Identifying Skin Images (Mahya Sadeghi, Parmit K. Chilana, M. Stella Atkins)....Pages 141-148 Back Matter ....Pages 149-149