RADLMT 2022 : 3rd International Workshop on Recent advances in deep learning methods and techniques for medical image analysis

With the advancement in biomedical imaging, the amount of data generated is increasing in biomedical engineering. For example, data can be generated by multimodality image techniques, e.g., ranging from Computed Tomography (CT), Magnetic Resonance Imaging (MR), Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. This poses a great challenge to develop new advanced imaging methods and computational models for efficient data processing, analysis, and modeling in clinical applications and understanding the underlying biological process. In recent years, deep learning is a rapidly advancing field in terms of both methodological development and practical applications. It allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It can implicitly capture intricate structures of large-scale data and is ideally suited to some of the hardware architectures currently available. The focus of this workshop is to carry out the research article, which could be more focused on the latest medical image analysis techniques based on Deep learning. In recent years, researchers have widely used the Deep Learning method and its variants. This Issue intends to bring new DL algorithms with some Innovative Ideas and find out the core problems in medical image analysis. Recommended topics include (but are not limited to) the following:

Application of deep learning in biomedical engineering
Transfer learning and multi-task learning
Joint semantic segmentation, object detection, and scene recognition on biomedical images
Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming
New models or new structures of the convolutional neural network
Visualization and explainable deep neural network

Organizers:
Yu-Dong Zhang, UK
University of Leicester
Email:[email protected]

Chenxi Huang
Xiamen University, China
Email: [email protected]