Special Issue on Deep Learning for Medical Image Analysis
摘要截稿:
全文截稿: 2018-05-31
影响因子: 4.438
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 计算机:人工智能 - 2区
Overview
In medical image analysis, the accurate diagnosis of a disease depends on two aspects: medical image acquisition and medical image interpretation. Medical image acquisition has grown substantially over recent years, with devices acquiring data at faster rates and increased resolution. The medical image interpretation has only recently begun to benefit from computer technology, and most interpretations on medical images are performed by physicians. However, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect abnormalities, and to quantify measurements and changes over time. Computerized tools, specifically image analysis and machine learning, are the key enablers to improve diagnosis, by facilitating identification of the findings that require treatment and to support the expert’s workflow. Among these tools, recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications.
The special issue seeks for original contribution of works which addresses the challenges from the deep learning techniques for medical image analysis. Papers on pure medical imaging would be out of the scope of this special issue. The list of possible topics includes, but not limited to:
- Convolutional Neural Networks (CNNs) in diseases diagnosis
- CNNs in medical image segmentation, fusion, shape modeling, and etc.
- Other deep learning networks in diseases diagnosis
- Unsupervised deep learning methods in medical image analysis
- Supervised deep learning methods in medical image analysis
- Transfer learning and fine-tuning methods in medical image analysis