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Multimodal learning for clinical dec...

(1998 :)

Multimodal learning for clinical decision support and clinical image-based procedures10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multimodal learning for clinical decision support and clinical image-based proceduresedited by Tanveer Syeda-Mahmood ... [et al.].
其他題名:	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 /
其他題名:	ML-CDS 2020
其他作者:	Syeda-Mahmood, Tanveer.
團體作者:
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xii, 138 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Diagnostic imagingCongresses.Data processing
電子資源:	https://doi.org/10.1007/978-3-030-60946-7
ISBN:	9783030609467$q(electronic bk.)

Multimodal learning for clinical decision support and clinical image-based procedures10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
Multimodal learning for clinical decision support and clinical image-based procedures10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /[electronic resource] :ML-CDS 2020edited by Tanveer Syeda-Mahmood ... [et al.]. - Cham :Springer International Publishing :2020. - xii, 138 p. :ill., digital ;24 cm. - Lecture notes in computer science,124450302-9743 ;. - Lecture notes in computer science ;4891..

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.

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.

ISBN: 9783030609467$q(electronic bk.)

Standard No.: 10.1007/978-3-030-60946-7doiSubjects--Topical Terms:

445765
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53

Dewey Class. No.: 616.0757

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