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 / edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi.

Format:

Book

Contributor:

Suzuki, K. (Kenji), Editor.

Reyes, Mauricio, Editor.

Syeda-Mahmood, Tanveer, Editor.

Konukoglu, Ender, Editor.

Glocker, Ben, Editor.

Wiest, Roland., Editor.

Gur, Yaniv., Editor.

Greenspan, Hayit, Editor.

Madabhushi, Anant, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11797

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11797

Language:

English

Subjects (All):

Artificial intelligence.

Machine theory.

Medical informatics.

Computer vision.

Artificial Intelligence.

Formal Languages and Automata Theory.

Health Informatics.

Computer Vision.

Local Subjects:

Artificial Intelligence.

Formal Languages and Automata Theory.

Health Informatics.

Computer Vision.

Physical Description:

1 online resource (XVI, 93 pages) : 40 illustrations, 35 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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. .

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.

Other Format:

Printed edition:

ISBN:

978-3-030-33850-3

9783030338503

Access Restriction:

Restricted for use by site license.