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Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support - Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

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This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.
The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. 

List of contents

Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019).- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification.- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics.- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis.- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection.- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules.- Deep neural network or dermatologist?.- Towards Interpretability of Segmentation Networks by analyzing DeepDreams.- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019).- Towards Automatic Diagnosis from Multi-modal Medical Data.- Deep Learning based Multi-Modal Registration for Retinal Imaging.-Automated Enriched Medical Concept Generation for Chest X-ray Images.

Product details

Assisted by ETH Zurich (Editor), Ben Glocker (Editor), Hayit Greenspan (Editor), Yaniv Gur (Editor), Ender Konukoglu (Editor), Anant Madabhushi (Editor), Maurici Reyes (Editor), Mauricio Reyes (Editor), Kenji Suzuki (Editor), Tanveer Syeda-Mahmood (Editor), Tanveer Syeda-Mahmood et al (Editor), Roland Wiest (Editor)
Publisher Springer, Berlin
 
Languages English
Product format Paperback / Softback
Released 01.12.2019
 
EAN 9783030338497
ISBN 978-3-0-3033849-7
No. of pages 93
Dimensions 155 mm x 6 mm x 235 mm
Weight 184 g
Illustrations XVI, 93 p. 40 illus., 35 illus. in color.
Series Lecture Notes in Computer Science
Image Processing, Computer Vision, Pattern Recognition, and Graphics
Subject Natural sciences, medicine, IT, technology > IT, data processing > IT

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