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Found 5 papers, 0 papers with code
Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI
In this work, we consider two pre-training approaches for driving a DL model to learn different representations using: a) regression loss that exploits spatial dependencies within an image and b) contrastive loss that exploits semantic similarity between pairs of images.
A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images
Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer.
White matter hyperintensities volume and cognition: Assessment of a deep learning based lesion detection and quantification algorithm on the Alzheimers Disease Neuroimaging Initiative
The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used.
A Contrast Synthesized Thalamic Nuclei Segmentation Scheme using Convolutional Neural Networks
The SCS network demonstrated a significant atrophy in Ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P=0. 01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS network showed spurious atrophy of the Ventral posterior lateral nucleus.
A Comparison of Deep Learning Convolution Neural Networks for Liver Segmentation in Radial Turbo Spin Echo Images
Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition.
