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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 4-8, 2020, Proceedings / edited by Tanveer Syeda-Mahmood, Klaus Drechsler, Hayit Greenspan, Anant Madabhushi, Alexandros Karargyris, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, Miguel Ángel González Ballester, Marius Erdt.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12445
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12445
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Computer vision.
- Social sciences-Data processing.
- Bioinformatics.
- Database management.
- Artificial Intelligence.
- Computer Vision.
- Computer Application in Social and Behavioral Sciences.
- Computational and Systems Biology.
- Database Management.
- Local Subjects:
- Artificial Intelligence.
- Computer Vision.
- Computer Application in Social and Behavioral Sciences.
- Computational and Systems Biology.
- Database Management.
- Physical Description:
- 1 online resource (XII, 138 pages) : 4 illustrations
- Edition:
- 1st ed. 2020.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- System Details:
- text file PDF
- 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.
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-60946-7
- 9783030609467
- Access Restriction:
- Restricted for use by site license.
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