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.
