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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 48, 2020, Proceedings 1st ed. 2020 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 138 pages, height x width: 235x155 mm, weight: 454 g, 4 Illustrations, black and white; XII, 138 p. 4 illus., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 12445
  • Izdošanas datums: 04-Oct-2020
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030609456
  • ISBN-13: 9783030609450
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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 48, 2020, Proceedings 1st ed. 2020Palielināt
  • Formāts: Paperback / softback, 138 pages, height x width: 235x155 mm, weight: 454 g, 4 Illustrations, black and white; XII, 138 p. 4 illus., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 12445
  • Izdošanas datums: 04-Oct-2020
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030609456
  • ISBN-13: 9783030609450
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783030609450.html
Keywords:

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.

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.