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

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

Publisher: Springer International Publishing
Year: 2019
Tongue: English
Leaves: 103
Series: Lecture Notes in Computer Science 11797
Edition: 1st ed. 2019
Category: Library

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✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xvi
Front Matter ....Pages 1-1
Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification (Fabian Eitel, Kerstin Ritter, for the Alzheimer’s Disease Neuroimaging Initiative (ADNI))....Pages 3-11
UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics (Hugo Yeche, Justin Harrison, Tess Berthier)....Pages 12-20
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis (Hyebin Lee, Seong Tae Kim, Yong Man Ro)....Pages 21-29
Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection (Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina, Sergey Morozov, Victor Gombolevsky, Valeria Chernina et al.)....Pages 30-38
Guideline-Based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules (Peifei Zhu, Masahiro Ogino)....Pages 39-47
Deep Neural Network or Dermatologist? (Kyle Young, Gareth Booth, Becks Simpson, Reuben Dutton, Sally Shrapnel)....Pages 48-55
Towards Interpretability of Segmentation Networks by Analyzing DeepDreams (Vincent Couteaux, Olivier Nempont, Guillaume Pizaine, Isabelle Bloch)....Pages 56-63
Front Matter ....Pages 65-65
Towards Automatic Diagnosis from Multi-modal Medical Data (Jiang Tian, Cheng Zhong, Zhongchao Shi, Feiyu Xu)....Pages 67-74
Deep Learning Based Multi-modal Registration for Retinal Imaging (Mustafa Arikan, Amir Sadeghipour, Bianca Gerendas, Reinhard Told, Ursula Schmidt-Erfurt)....Pages 75-82
Automated Enriched Medical Concept Generation for Chest X-ray Images (Aydan Gasimova)....Pages 83-92
Back Matter ....Pages 93-93

✦ Subjects

Computer Science; Mathematical Logic and Formal Languages; Health Informatics; Image Processing and Computer Vision

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