Next, a dual discriminator based adversarial training procedure, which jointly considers an image discriminator that can maintain the local consistency at frame-level and a video discriminator that can enforce the global coherence of temporal dynamics, is employed to enhance the future frame prediction.
Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans.
no code implementations • 15 Jan 2020 • Haoran Sun, Xueqing Liu, Xinyang Feng, Chen Liu, Nanyan Zhu, Sabrina J. Gjerswold-Selleck, Hong-Jian Wei, Pavan S. Upadhyayula, Angeliki Mela, Cheng-Chia Wu, Peter D. Canoll, Andrew F. Laine, J. Thomas Vaughan, Scott A. Small, Jia Guo
Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases.
In neuroimaging studies, the human cortex is commonly modeled as a sphere to preserve the topological structure of the cortical surface.
Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.
Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis.