ArticleJournal of medical imaging (Bellingham, Wash.)2023
CaraNet: context axial reverse attention network for segmentation of small medical objects.
Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- MBRSNet: Boundary-Aware Multi-Task Learning with Signed Distance Field Regression for Polyp Segmentation.Journal of imaging · 2026Article
- Automated and quantitative segmentation of colorectal polyps via an information-guided deep learning network with boundary perception.Quantitative imaging in medicine and surgery · 2026Article
- MCM-UNet++: A Hybrid Soft Computing Framework for Multi-Scale Polyp Segmentation via Enhanced Global Context and Adaptive Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- FDSS-Net: feature enhancement and dual-stream semantic mixture network for polyp segmentation.Scientific reports · 2026Article
- Article
- SegFormer-based boundary-aware polyp segmentation with adaptive multi-branch fusion.Scientific reports · 2026Article
- A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.Scientific reports · 2026Article
- MDPNet: a multi-scale difference perception network for esophageal cancer segmentation in CT images.BMC medical imaging · 2026Article
- PrysmNet a polyp refining system using salience and multimodal guidance for reproducible cross domain segmentation.NPJ digital medicine · 2026Article
- BoxCell: Leveraging SAM for Cell Segmentation with Box Supervision.Scientific reports · 2025Article
- A multi-scale attention-based Swin transformer model for medical images segmentation.Scientific reports · 2025Article
- FFTMed: leveraging fast-fourier transform for a lightweight and adversarial-resilient medical image segmentation framework.Scientific reports · 2025Article
- Spine endoscopic atlas: an open-source dataset for surgical instrument segmentation.Scientific data · 2025Article
- InCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images.Journal of imaging informatics in medicine · 2025Article
- ColonNeXt: Fully Convolutional Attention for Polyp Segmentation.Journal of imaging informatics in medicine · 2025Article
- Multi-scale fusion semantic enhancement network for medical image segmentation.Scientific reports · 2025Article
- EFCNet enhances the efficiency of segmenting clinically significant small medical objects.Scientific reports · 2025Article
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3 authors.
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Abstract
Purpose: Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes, shapes, and scanning modalities. Recently, many convolutional neural networks have been designed for segmentation tasks and have achieved great success. Few studies, however, have fully considered the sizes of objects; thus, most demonstrate poor performance for small object segmentation. This can have a significant impact on the early detection of diseases. Approach: We propose a context axial reverse attention network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. CaraNet applies axial reserve attention and channel-wise feature pyramid modules to dig the feature information of small medical objects. We evaluate our model by six different measurement metrics. Results: We test our CaraNet on segmentation datasets for brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB). Our CaraNet achieves the top-rank mean Dice segmentation accuracy, and results show a distinct advantage of CaraNet in the segmentation of small medical objects. Conclusions: We proposed CaraNet to segment small medical objects and outperform state-of-the-art methods.
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