Advances in Deep Learning Methods for Medical Image Analysis

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Background

Semantic image segmentation is one of the most common basic tasks in the field of medical image analysis and has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. But achieving this segmentation automatically has been proven very challenging due to the large variation of anatomy across different subjects and image modalities.

Recent advances in deep learning have made it possible to significantly improve the performance of semantic segmentation methods in the field, thanks to the data-driven approaches of hierarchical feature learning in deep learning frameworks. The main scope of this research topic is to help advance the scientific researches within the broad field of machine learning and deep learning in medical image segmentation. We hope that this research topic will facilitate translating medical imaging researches boosted by machine learning from bench to bedside.

This research topic will focus on major trends and challenges in this area and will present works aiming to identify new cutting-edge techniques and their uses in medical image segmentation.
• The first academic objective of the research topic is to bring together researchers in medical imaging, machine learning, pattern recognition, computer vision, and artificial intelligence communities to discuss the new techniques of medical image segmentation for clinical decision support in diagnosis and therapy, and image-guided interventions and surgery.
• The second objective is to explore new paradigms for the design of medical image segmentation systems that exploit the latest results in machine learning and deep learning. This research topic will feature demonstrations of the state-of-the-art deep learning systems and concepts that are applied to semantic/pathological/diagnostic medical image segmentation. Topics of interests include, but are not limited to, deep learning methods (e.g., self-supervised learning, transfer learning, multi-task learning, multi-modality/multi-view fusion, automated/interactive segmentation) for brain image segmentation, cellular image segmentation, big medical imaging data analytics, molecular imaging, and their applications to computer-aided diagnosis and analysis, etc.

Keywords: self-supervised learning, transfer learning, multi-task learning, multi-modality, multi-view fusion, automated segmentation, interactive segmentation

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