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Found 6 papers, 0 papers with code
Revealing Cortical Layers In Histological Brain Images With Self-Supervised Graph Convolutional Networks Applied To Cell-Graphs
Identifying cerebral cortex layers is crucial for comparative studies of the cytoarchitecture aiming at providing insights into the relations between brain structure and function across species.
NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images
Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies.
MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging
In comparative neuroanatomy, the characterization of brain cytoarchitecture is critical to a better understanding of brain structure and function, as it helps to distill information on the development, evolution, and distinctive features of different populations.
Endoscopy artifact detection (EAD 2019) challenge dataset
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs.
Analytic heuristics for a fast DSC-MRI
In this paper we propose a deterministic approach for the reconstruction of Dynamic Susceptibility Contrast magnetic resonance imaging data and compare it with the compressed sensing solution existing in the literature for the same problem.
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis.
