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New CNN predicts preterm infant brain injury from MRI

By PulseAugur Editorial · [3 sources] ·

Researchers have developed GloResNet, a lightweight 3D convolutional neural network designed to predict brain injury in preterm infants using T2-weighted MRI scans. The model, based on ResNet-10 and pretrained on MedicalNet, incorporates a global manifold mapping strategy to preserve topological features while standardizing image appearance. In cross-validation tests, GloResNet achieved an average accuracy of 75.18%, demonstrating its potential as a non-invasive screening tool for neonatal brain injury. AI

IMPACT Offers a potential new non-invasive tool for early detection of brain injury in newborns.

RANK_REASON The cluster contains an academic paper detailing a new model for a specific application.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New CNN predicts preterm infant brain injury from MRI

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction

    This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We propose GloResNet, a lightweight 3D CNN based on ResNet-10, pretrained on MedicalNet to address data scarcity. A global manifold …

  2. arXiv cs.CV TIER_1 English(EN) · Boyu Yuan, Jiamiao Lu, Weichuan Zhang, Benqing Wu, Tuo Wang, Changshan Wang, Changming Sun, Liang Guo ·

    GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction

    arXiv:2606.02498v1 Announce Type: new Abstract: This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We propose GloResNet, a lightweight 3D CNN based on ResNet-10, pretrained on Medical…

  3. arXiv cs.CV TIER_1 English(EN) · Liang Guo ·

    GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction

    This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We propose GloResNet, a lightweight 3D CNN based on ResNet-10, pretrained on MedicalNet to address data scarcity. A global manifold …

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