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.)

Published Cham : Springer, 2020

Click on the following:
Springer eBooks
Springer Computer Science eBooks

Copies

Description 1 online resource (147 pages) Series Lecture notes in computer science ; 12445 LNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics Lecture notes in computer science ; 12445. LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics. Contents Intro -- Additional Workshop Editors -- Preface ML-CDS 2020 -- Organization -- Preface CLIP 2020 -- Organization -- Contents -- CLIP 2020 -- Optimal Targeting Visualizations for Surgical Navigation of Iliosacral Screws -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion and Conclusion -- References -- Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records -- 1 Introduction -- 2 Method -- 2.1 Data of T2DM Studies -- 2.2 Abdominal Segmentations -- 2.3 EMR Feature Extraction -- 3 Experiments -- 3.1 Experimental Design 3.2 Implementation Details and Metric -- 3.3 Results and Analyses -- 4 Discussion and Conclusion -- References -- A Radiomics-Based Machine Learning Approach to Assess Collateral Circulation in Ischemic Stroke on Non-contrast Computed Tomography -- 1 Introduction -- 2 Materials and Methods -- 2.1 Scanning Protocols -- 2.2 Ground Truth Labels -- 2.3 Mapping of ASPECTS Regions -- 2.4 Pre-processing -- 2.5 Image Features -- 2.6 Classification of Collaterals -- 3 Results -- 4 Discussion -- References Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 MER Acquisition and Preprocessing -- 2.3 MRI Data Processing -- 2.4 Active Contours Fitting -- 2.5 MER-based Fitting -- 2.6 Evaluation Procedure -- 3 Results and Discussions -- 3.1 STN Segmentation -- 3.2 MER-based Fitting -- 4 Conclusion -- References -- 3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research -- 1 Introduction -- 2 Methods 3 Results and User Studies Applications -- 4 Conclusion -- References -- Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision -- 1 Introduction -- 2 Prior Work -- 3 Method -- 3.1 Dataset -- 3.2 Variational Autoencoder -- 3.3 Training -- 3.4 Model Evaluation -- 4 Results -- 5 Conclusion and Future Work -- References -- Single-Shot Deep Volumetric Regression for Mobile Medical Augmented Reality -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Acquisition and Preprocessing -- 2.2 Android Application -- 2.3 Server Backend -- 3 Results 3.1 Quantitative Evaluation -- 3.2 Qualitative Evaluation -- 4 Discussion -- 5 Conclusion -- References -- A Baseline Approach for AutoImplant: The MICCAI 2020 Cranial Implant Design Challenge -- 1 Introduction -- 2 Dataset -- 3 Method -- 4 Experiments and Results -- 5 Conclusion and Future Improvement -- References -- Adversarial Prediction of Radiotherapy Treatment Machine Parameters -- 1 Introduction -- 2 Methods -- 2.1 Patient Data Preparation and Treatment Planning -- 2.2 Data Reformatting for Supervised Learning -- 2.3 Deep Learning Network and Training -- 3 Results 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 Notes International conference proceedings "Due to the COVID-19 pandemic, the workshop was held as an online-only meeting to contribute to slowing down the spread of the virus."--Preface 3.1 Plan Quality Overview via Dose Volume Histograms Includes author index Online resource; title from PDF title page (SpringerLink, viewed December 2, 2020) Subject Diagnostic imaging -- Data processing -- Congresses Computer-assisted surgery -- Congresses Diagnostic imaging -- Data processing Computer-assisted surgery Application software Artificial intelligence Bioinformatics Database management Optical data processing Genre/Form Electronic books proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. Form Electronic book Author Syeda-Mahmood, Tanveer. Drechsler, Klaus. Greenspan, Hayit. Madabhushi, Anant. Karargyris, Alexandros Linguraru, Marius George. Oyarzun Laura, Cristina Shekhar, Raj (Biomedical engineer) Wesarg, Stefan González Ballester, Miguel Ángel CLIP (Workshop) (9th : 2020 : Online) International Conference on Medical Image Computing and Computer-Assisted Intervention (23rd : 2020 : Online) ISBN 9783030609467 3030609464

Other Titles ML-CDS 2020 CLIP 2020

  Permalink