UID:
almahu_9948595010802882
Format:
XII, 138 p. 4 illus.
,
online resource.
Edition:
1st ed. 2020.
ISBN:
9783030609467
Series Statement:
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12445
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030609450
Additional Edition:
Printed edition: ISBN 9783030609474
Language:
English
DOI:
10.1007/978-3-030-60946-7
URL:
https://doi.org/10.1007/978-3-030-60946-7
