Deep Learning Techniques for Medical Image Analysis

Dear Colleagues,

In recent years, deep learning techniques have been widely used in medical image analysis. These techniques employ deep neural networks to automatically extract multi-level, multi-scale, abundant information (features) from image data, which is hard for conventional machine learning techniques which use hand-crafted feature parameters, including supervised learning (with task-driven models), unsupervised or generative learning (with data-driven models), semi-supervised learning (with hybrid task-driven and data-driven models), reinforcement learning (with environment-driven models), and physics-informed learning (hybrid task-driven and physics-driven models). The analyzed imaging modalities can include structural imaging such as X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound imaging, and ultrasound computed tomography, as well as functional imaging such as functional MRI, positron emission tomography (PET), single-photon emission computed tomography (SPECT), and functional ultrasound imaging, whether two-dimensional, three-dimensional, or even four-dimensional (three-dimensional plus temporal). The vast applications of deep learning techniques in medical image analysis cover lesion detection and segmentation, disease diagnosis, treatment monitoring, efficacy evaluation, prognostic prediction, and even biomechanical analysis. In addition to medical image post-processing, deep learning techniques can also be applied to the front-end (e.g., image reconstruction) to enhance the quality of medical imaging.

Given the high level of research interest and clinical application prospects, deep learning techniques have continued to develop, especially in the field of medical image analysis. This Special Issue aims to report on state-of-the-art deep learning techniques applied to medical image analysis. Contributions related to deep learning techniques in medical image analysis are welcome.

Dr. Zhuhuang Zhou
Guest Editor

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• deep learning
• supervised learning
• unsupervised learning
• semi-supervised learning
• self-supervised learning
• generative learning
• deep neural networks
• convolutional neural networks
• physics-informed neural networks
• X-ray imaging
• computed tomography (CT)
• magnetic resonance imaging (MRI)
• ultrasound imaging
• ultrasound computed tomography
• functional MRI
• positron emission tomography (PET)
• single-photon emission computed tomography (SPECT)
• functional ultrasound imaging
• image reconstruction