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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 / Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood et al. (Eds.).
iMIMIC (Workshop) (2nd : 2019 : Shenzhen Shi, China); Suzuki, Kenji, editor.; Reyes, Mauricio, editor.; Syeda-Mahmood, Tanveer, editor.; ML-CDS (Workshop) (9th : 2019 : Shenzhen Shi, China), jointly held conference.; International Conference on Medical Image Computing and Computer-Assisted Intervention (22nd : 2019 : Shenzhen Shi, China), jointly held conference.
2019
RC78.7.D53
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Title
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 / Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood et al. (Eds.).
Meeting Name
iMIMIC (Workshop) (2nd : 2019 : Shenzhen Shi, China)
ISBN
9783030338503 (electronic book)
3030338509 (electronic book)
9783030338497
DOI
https://doi.org/10.1007/978-3-030-33850-3
https://doi.org/10.1007/978-3-030-33
Published
Cham, Switzerland : Springer, 2019.
Language
English
Description
1 online resource (xvi, 93 pages) : illustrations.
Item Number
10.1007/978-3-030-33850-3 doi
10.1007/978-3-030-33
Call Number
RC78.7.D53
Dewey Decimal Classification
616.07/57
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.
Note
International conference proceedings.
Includes author index.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 30, 2019).
Added Author
Suzuki, Kenji, editor.
Reyes, Mauricio, editor.
Syeda-Mahmood, Tanveer, editor.
Added Meeting Name
ML-CDS (Workshop) (9th : 2019 : Shenzhen Shi, China), jointly held conference.
International Conference on Medical Image Computing and Computer-Assisted Intervention (22nd : 2019 : Shenzhen Shi, China), jointly held conference.
Series
Lecture notes in computer science ; 11797.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
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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.

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