To solve the two problems, we propose an iterative dual regression network for an adaptive and precision blur kernel estimation, which improves the speed of kernel estimation by learning a dual mapping. Specifically, we design a Predictor-Generator structure: the Predictor, through several iterations...
Hi. I am writing a C-RNN regression learning code with single matrix input - dual scalar output. The loaded "paddedData2.mat" file is saved as paddedData, and it is stored as an N X 3 cell, as shown in the attached image. The input matrix used for training is the 3rd colum...
dr_stage1_subject[#SUB].txt- the timeseries outputs of stage 1 of the dual-regression. One text file per subject, each containing columns of timeseries - one timeseries per group-ICA component.These timeseries can be fed into furthernetwork modelling, e.g., taking the N timeseries and ...
Network-specific effects of age and in-scanner subject motion: A resting-state fMRI study of 238 healthy adult Resting state FMRISubject motionICADual-regressionCognitive aging is accompanied by a range of structural and functional differences in the brain, even in ... AM Mowinckel,T Espeseth,LT...
A Dual Path Network (DPN) is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that ResNets enables feature re-usage while DenseNet enables new feature exploration, and both are important for learning good representations. To enjoy the ben...
proposed a CNN-based regression model to directly learn the complex mapping from the input image pair to their corresponding deformation field. The evaluation of this model showed a maximal 2.6% improved dice similarity coefficient (DSC) in the white matter, gray Matter and cerebrospinal fluid ...
We set coefficient alpha = 0.1, 0.5, 1.0, 2.0 for ridge regression. Ma et al. BMC Bioinformatics 2022, 23(Suppl 4):129 Page 10 of 13 Clinical cancer data preparation We conducted a case study on clinical cancer patients using DualGCN. First, we curated data of patients whose ...
regression (Fig.S3C, D), and finally multivariate Cox regression analysis was performed with 2 genes (PSMD11 and PSMD2). The result showed that only PSMD2 could be served as an independent prognostic factor for NSCLC (P < 0.05, Fig.2Band TableS3). And the PPI network analysis found...
To mimic treatment rather ivirI(n3t)hains dpa2r.t2i cμuMlarfoarsscaoym, spuoruamndin5.(7T)hheasdloapneamvewraagseaIlCso50dveatleuremoifn7e4d.5fr μoMm, compared with 61.5 μM for zanam- a linear regression of median-effect plots (Equation (4)), as they reflect the ...
The positive and negative samples are determined by calculating the intersection over union (IoU) of the anchor boxes and the ground truth boxes, the losses are calculated, and the positions of the detected objects are corrected based on the regression offsets. Anchor-based detectors have been ...