Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings / Tanveer Syeda-Mahmood, Klaus Drechsler et al. (eds.).
ML-CDS (Workshop) (10th : 2020 : Online); Syeda-Mahmood, Tanveer, editor.; Drechsler, Klaus, editor.; Greenspan, Hayit, editor.; Madabhushi, Anant, editor.; Karargyris, Alexandros., editor.; Linguraru, Marius George, editor.; Oyarzun Laura, Cristina., editor.; Shekhar, Raj, editor.; Wesarg, Stefan., editor.; González Ballester, Miguel Ángel., editor.; Erdt, Marius, editor.
2020
Q334-342
Available Online

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Title Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings / Tanveer Syeda-Mahmood, Klaus Drechsler et al. (eds.).
Meeting Name ML-CDS (Workshop) (10th : 2020 : Online)
ISBN 9783030609467
3030609464
9783030609467
3030609456
9783030609450
Imprint Cham : Springer, 2020.
Language English
Description 1 online resource (XII, 138 pages) : illustrations.
Call Number Q334-342
Dewey Decimal Classification 006.3
Summary This book constitutes the refereed joint proceedings of the 10th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th International Workshop on Clinical Image-Based Procedures, CLIP 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. The 4 full papers presented at ML-CDS 2020 and the 9 full papers presented at CLIP 2020 were carefully reviewed and selected from numerous submissions to ML-CDS and 10 submissions to CLIP. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Added Author Syeda-Mahmood, Tanveer, editor.
Drechsler, Klaus, editor.
Greenspan, Hayit, editor.
Madabhushi, Anant, editor.
Karargyris, Alexandros., editor.
Linguraru, Marius George, editor.
Oyarzun Laura, Cristina., editor.
Shekhar, Raj, editor.
Wesarg, Stefan., editor.
González Ballester, Miguel Ángel., editor.
Erdt, Marius, editor.
Series LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics ; 12445.
Available in Other Form Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings
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CLIP 2020
Optimal Targeting Visualizations for Surgical Navigation of Iliosacral Screws
Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records
A Radiomics-based Machine Learning Approach to Assess Collateral Circulation in Stroke on Non-contrast Computed Tomography
Image-based Subthalamic Nucleus Segmentation for Deep Brain Surgery With Electrophysiology Aided Refinement
3D Slicer Craniomaxillofacial Modules Support Patient-specific Decision-making for Personalized Healthcare in Dental Research
Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision
Single-shot Deep Volumetric Regression for Mobile Medical Augmented Reality
A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge
Adversarial Prediction of Radiotherapy Treatment Machine Parameters
ML-CDS 2020
Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data
Towards Automated Diagnosis with Attentive Multi-Modal Learning Using Electronic Health Records and Chest X-rays
LUCAS: LUng CAncer Screening with Multimodal Biomarkers
Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound.

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