review state-of-the-art deep-learning-empowered computational spectral imaging methods.They are further divided into amplitude-coded,phase-coded,and wavelength-coded methods,based on different light properties used for encoding.To boost future researches,we've also organized publicly available spectral ...
6月17日,浙江大学光电科学与工程学院郝翔课题组综述文章《Spectral imaging with deep learning》发表于《Light: Science & Applications》杂志第六期封面。该研究回顾了光谱成像技术应用深度学习的最新进展,对基于深度学习的光谱成像技术进行了梳理。研究对深度学习光谱成像的各种技术路线进行了原理阐述、研究总结,并整理了...
of-the-art deep-learning-empowered computational spectral imaging methods. They are further divided into amplitude-coded, phase-coded, and wavelength-coded methods, based on different light properties used for encoding. To boost future researches, we’ve also organized publicly available spectral ...
To meet the growing cardiovascular needs of healthcare providers, Canon Medical Systems USA, Inc. is breaking ground once again with the introduction of Deep Learning Spectral CT for cardiovascular imaging. The Aquilion ONE/PRISM Edition now offers one-beat spectral cardiac CT imaging. Thanks to th...
Medical imaging is an invaluable resource in medicine as it enables to peer inside the human body and provides scientists and physicians with a wealth of i
We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative reconstruction on image quality in CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). For 220 patients with suspected PE, CTPA studies were reconstructed using filtered back projection...
3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients (http://t.cn/RWAkkPX) Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (http://t.cn/RWAFyHc) ...
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity deep-learningneural-networkcnnpytorchremote-sensingneural-networksmultispectralneural-networks-from-scratchunsupervised-deep-learningpansharpeningworldview-3worldview-2geoeye-1 ...
Splane employs a graph convolutional network (GCN) approach44,45 and an adversarial learning algorithm46 to identify spatial domains by jointly analyzing multiple ST slices (Fig. 1c, Methods). First, for each ST slice, Splane calculates an adjacency matrix of cells/spots based on their distance...
Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve the segmentation. Recently, deep learning-based approaches have presented the state-of-the...