Special Issue on Deep Learning for Medical Image Computing
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
Deep learning is one of the most important breakthroughs in the field of artificial intelligence over the last decade. It has achieved great success in different tasks in computer vision and image processing. Methods and models on medical image analysis also benefit from the powerful representation learning capability of deep learning techniques. Not only there has been a constantly growing flow of related research papers, but also substantial progress has been achieved in real-world applications such as radiotherapy planning, histological image understanding and retina image recognition.
While substantial progress has been achieved in medical image analysis with deep learning, many issues still remain and new problems emerge. For instance, the scalability of 3D deep networks to handle thin-layer CT images, the limited training samples of medical images compared with other image understanding tasks, the significant class imbalance of many medical classification problems, noisy and weakly supervisions for training deep learning models from medical reports. The accuracy and efficiency of deep learning models for medical image analysis also see large room for improvement.
This special issue presents a great platform to make a definitive statement about the state of the art by providing a significant collective contribution to this emerging field of study. Specifically, we aim to solicit original contributions that: (1) present state-of-the-art deep learning methods for medical image analysis; (2) develop novel methods and applications; (3) survey the recent progress in this area; and (4) establish benchmark datasets.
Topics of Interest:
The topics of interest include (but not limited to):
-- Theoretical analysis of deep learning models for medical image analysis
--Evaluation of deep learning models for medical image analysis
--New object functions and formulations for medical image analysis
--New network structures and training schema for medical image analysis
--Deep learning methods for medical image classification
--Deep learning methods for medical image segmentation
--Deep learning methods for detection from medical images
--Deep learning methods for medical image registration
--Deep learning methods for 4D medical image sequence analysis
--Generative models for medical images
--Deep adversarial learning for medical image analysis
--Weakly or semi-supervised deep learning for medical image analysis