Submission Deadline: 30 September 2022 (closed)
In recent years, artificial intelligence has developed rapidly in the medical field, which is largely due to the development and progress of deep learning (DL) technology. Due to the rapid advancement of computer technology, medical imaging applications based on DL have become a new engine of innovation in the medical field. In the field of medical imaging, machine learning based on DL has a positive impact on the optimization of image reconstruction, lesion segmentation, computer-aided detection and computer-aided diagnosis. Deep learning (DL) combined with medical image analysis (MIA) can provide what is known as DL-MIA. Artificial intelligence has been applied to magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, mammography, color gastroscopy, multi-modal imaging and so on. At the same time, DL-MIA has applications in different medical fields, such as neurology, ophthalmology, cardiology, geriatrics, etc. At present, the commonly used DL technologies include convolutional neural network (CNN), recurrent neural network (RNN), and deep generation network (such as GAN, SAE, DBM, DBN). In the next few decades, DL-MIA technology may have a positive impact on the progress of medical image analysis, but there are still some challenges to be solved.
The purpose of this special issue is to collect the latest research results of key issues and topics related to medical images, and has the characteristics of flexibility, consistency, extensibility and universality. They can be regarding the classification, detection, and segmentation of medical images. We suggest that the authors provide as many research details as possible, a detailed research paper or a comprehensive review.
Papers are invited from the following suggested topics but not limited to:
· Detection classification and lesion staging using DL-MIA
· Artificial intelligence applied to image detection (including anatomical location and lesion location)
· Medical image reconstruction based on DL-MIA
· Medical image registration based on DL-MIA
· Medical image segmentation based on DL-MIA
· Disease classification based on MRI and CT scans
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