ArticleBiomedical engineering letters2024
CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation.
Article in Biomedical engineering letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A diffusion-conditioned representation learning framework for disease classification in medical imaging.BMC research notes · 2026Article
- ShuffleLeNet architecture for spinal cord injury classification and level detection.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- A multi-modal deep learning framework for enhanced breast cancer diagnosis using mammograms and clinical data.Scientific reports · 2026Article
- A novel sub-differentiable hausdorff loss combined with BCE for MRI brain tumor segmentation using UNet variants.Scientific reports · 2025Article
- SwinCLNet: a robust framework for brain tumor segmentation via shifted window attention and cross-scale fusion.Scientific reports · 2025Article
- Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification.Journal of imaging informatics in medicine · 2025Article
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4 authors.
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Abstract
Deep learning-based methods have recently shown great promise in medical image segmentation task. However, CNN-based frameworks struggle with inadequate long-range spatial dependency capture, whereas Transformers suffer from computational inefficiency and necessitate substantial volumes of labeled data for effective training. To tackle these issues, this paper introduces CI-UNet, a novel architecture that utilizes ConvNeXt as its encoder, amalgamating the computational efficiency and feature extraction capabilities. Moreover, an advanced attention mechanism is proposed to captures intricate cross-dimensional interactions and global context. Extensive experiments on two segmentation datasets, namely BCSD, and CT2USforKidneySeg, confirm the excellent performance of the proposed CI-UNet as compared to other segmentation methods.
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