Summary of Symbrain: a Large-scale Dataset Of Mri Images For Neonatal Brain Symmetry Analysis, by Arnaud Gucciardi and Safouane El Ghazouali and Francesca Venturini and Vida Groznik and Umberto Michelucci
Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis
by Arnaud Gucciardi, Safouane El Ghazouali, Francesca Venturini, Vida Groznik, Umberto Michelucci
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel annotated dataset of brain MRI images designed to advance the field of brain symmetry study in neonatal infants. The dataset, comprising cerebral images extracted as slices across selected portions of interest, is annotated with the brain’s midline. The goal is to improve diagnosis and treatment planning by training deep learning models to detect anomalies in medical MRI images. The authors leverage the Developing Human Connectome Project dataset to create a dataset that can be used to train computer vision-based models for neonatal cerebral MRI anomaly detection from postnatal infant scans. This work has implications for diagnosing potential clinical pathologies related to decreased brain symmetry, enabling more precise diagnosis and treatment planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a special set of pictures taken with a special machine called an MRI scanner. These pictures show the inside of newborn babies’ brains. The goal is to help doctors diagnose any problems in these tiny brains by looking at how symmetrical they are. Right now, it’s hard to compare baby brain images to adult brain images because of the big size difference. But this dataset (a collection of pictures) can help train computers to spot unusual patterns in these images, making it easier for doctors to diagnose and treat problems. This is important because some babies may have issues that need attention early on. |
Keywords
* Artificial intelligence * Anomaly detection * Attention * Deep learning
