Accuracy, efficiency and transferability are realistic issues in clinical practice, and research hotspots as well. Especially in developing countries, the number of clinicians is not enough to meet the population growth. One doctor has to diagnose dozens of patients every day, with traditional clinical methods. In recent years, artificial intelligence (AI) techniques, e.g., deep learning, tend to mature and have been applied in many clinical tasks successfully. Well-designed networks are able to detect various lesions in medical images, such as pulmonary nodule, diabetic retinopathy, and skin cancer. The accuracy and efficiency have also achieved or exceeded human experts. Introducing AI-aided methods to traditional clinical process would help reduce the burden of doctors, or improve diagnostic ability of inexperienced clinicians.
AI can be a sharp tool with sufficient data and deep architectures. But in many circumstances, models only give a simple result without any evidences, which is still unconvincing for clinical use. Moreover, challenging clinical data (small data, imbalanced data in rare diseases, unobvious lesions in early stage, multiple data source with different distributions, etc.) that cannot meet the requirements of AI algorithms are common. Existing intelligent solutions may be unstable or fail for those data. This requires novel computing techniques with robust, practical and trustworthy.
This Special Issue aims to invite original research papers that tackle challenging clinical data and scenarios, including theoretical research, practical models improvement, clinical-oriented data analysis, deployed digital system, and farsighted intelligent architecture. Submitted papers should clarify the substantively different from work that has already been published, or accepted for publication, or submitted in parallel to other conferences or journals.
The topics of interest include, but are not limited to:
- Robust models for small or imbalanced clinical data
- Interpretable algorithms with meaningful clinical outcomes
- Novel learning paradigms for rare diseases
- Multi-center/cross-regional clinical data fusion and decision support
- Collaborative computing on heterogeneous clinical data
- VR/AR computer-aided diagnosis system
- Real-time video assessment system
- Classification, detection, segmentation of lesions and tumors in early stage
- New measurements and computing methods for early diagnosis
- New technology for mining sensitive clinical biomarkers or patterns
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
- Submission deadline: Dec 20, 2020
- Notification of acceptance: Feb 25, 2021
- Submission of final revised paper: Mar 25, 2021
- Publication: As accepted, likely May 2021
- Prof. Honghao Gao, Shanghai University, China
- Prof. Wenbing Zhao, Cleveland State University, Ohio, USA
- Prof. Yuyu Yin, Hangzhou Dianzi University, China