mlsanityData to 14 Oct 2026

Deep learning-based neurodevelopmental assessment in preterm infants

arXiv 2601.11944 ↗First posted 17 Jan 2026cs.CVcited by 3

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Abstract (arXiv)

Preterm infants (born between 28 and 37 weeks of gestation) face elevated risks of neurodevelopmental delays, making early identification crucial for timely intervention. While deep learning-based volumetric segmentation of brain MRI scans offers a promising avenue for assessing neonatal neurodevelopment, achieving accurate segmentation of white matter (WM) and gray matter (GM) in preterm infants remains challenging due to their comparable signal intensities (isointense appearance) on MRI during early brain development. To address this, we propose a novel segmentation neural network, named Hierarchical Dense Attention Network. Our architecture incorporates a 3D spatial-channel attention mechanism combined with an attention-guided dense upsampling strategy to enhance feature discrimination in low-contrast volumetric data. Quantitative experiments demonstrate that our method achieves superior segmentation performance compared to state-of-the-art baselines, effectively tackling the challenge of isointense tissue differentiation. Furthermore, application of our algorithm confirms that WM and GM volumes in preterm infants are significantly lower than those in term infants, providing additional imaging evidence of the neurodevelopmental delays associated with preterm birth. The code is available at: https://github.com/ICL-SUST/HDAN.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 28 of its 118 references that are in the map. First posted on arXiv 17 Jan 2026.

Builds on

28 of 118 references.

References · 28

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16 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classificationcited by 1
  2. 2026GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury predictioncited by 1
  3. 2026Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classificationcited by 0