Due to numerous biomedical information sensing devices, such as Computed Tomography (CT), Magnetic Resonance (MR) Imaging, 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. a large amount of biomedical information was gathered these years. However, identifying how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling from the collected data is important for clinical applications and to understand the underlying biological processes.
Deep learning approaches have been rapidly developed in recent years, both in terms of methodologies and practical applications. Deep learning techniques provide computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. Deep Learning allows to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.
The purpose of this Article Collection is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of Deep learning and Computational Machine Learning, to solve problems in biomedical engineering. The ultimate goal is to promote research and development of deep learning for multimodal biomedical images by publishing high-quality research articles, reviews, or perspectives, among other article types, in this rapidly growing interdisciplinary field.
Topics include, but are not limited to:
- Theoretical understanding 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
- Multimodal imaging techniques (data acquisition, reconstruction, 2D, 3D, 4D imaging, etc.)
- Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic
analysis, quantitative measurements, image guidance of ultrasonography)
- Optimization by deep neural networks, Multi-dimensional deep learning
- New Model of New Structure of convolutional neural network
- Visualization and Explainable deep neural network
Due to numerous biomedical information sensing devices, such as Computed Tomography (CT), Magnetic Resonance (MR) Imaging, 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. a large amount of biomedical information was gathered these years. However, identifying how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling from the collected data is important for clinical applications and to understand the underlying biological processes.
Deep learning approaches have been rapidly developed in recent years, both in terms of methodologies and practical applications. Deep learning techniques provide computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. Deep Learning allows to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.
The purpose of this Article Collection is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of Deep learning and Computational Machine Learning, to solve problems in biomedical engineering. The ultimate goal is to promote research and development of deep learning for multimodal biomedical images by publishing high-quality research articles, reviews, or perspectives, among other article types, in this rapidly growing interdisciplinary field.
Topics include, but are not limited to:
- Theoretical understanding 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
- Multimodal imaging techniques (data acquisition, reconstruction, 2D, 3D, 4D imaging, etc.)
- Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic
analysis, quantitative measurements, image guidance of ultrasonography)
- Optimization by deep neural networks, Multi-dimensional deep learning
- New Model of New Structure of convolutional neural network
- Visualization and Explainable deep neural network
