ArticleDiagnostics (Basel, Switzerland)2022
AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Accurate Asthma-COPD Overlap Classification via Deep Transfer Learning in Medical Image Segmentation.International journal of chronic obstructive pulmonary disease · 2026Article
- Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.Scientific reports · 2025Article
- AI-Powered histopathology slide image interpretation in oncology: A comprehensive knowledge mapping and bibliometric analysis.Digital healthArticle
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Semantic segmentation of biomedical images found its niche in screening and diagnostic applications. Recent methods based on deep learning convolutional neural networks have been very effective, since they are readily adaptive to biomedical applications and outperform other competitive segmentation methods. Inspired by the U-Net, we designed a deep learning network with an innovative architecture, hereafter referred to as AID-U-Net. Our network consists of direct contracting and expansive paths, as well as a distinguishing feature of containing sub-contracting and sub-expansive paths. The implementation results on seven totally different databases of medical images demonstrated that our proposed network outperforms the state-of-the-art solutions with no specific pre-trained backbones for both 2D and 3D biomedical image segmentation tasks. Furthermore, we showed that AID-U-Net dramatically reduces time inference and computational complexity in terms of the number of learnable parameters. The results further show that the proposed AID-U-Net can segment different medical objects, achieving an improved 2D F
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Registered trials
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