ArticleProceedings. IEEE International Symposium on Biomedical Imaging2025
COUPLED SWIN TRANSFORMERS AND MULTI-APERTURES NETWORK(CSTA-NET) IMPROVES MEDICAL IMAGE SEGMENTATION.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Robust analysis of the tumor spectrum in a preclinical model of breast cancer reveals stable subtypes with distinct growth patterns.Computer methods and programs in biomedicine · 2026Article
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4 authors.
Funding
Abstract
Vision Transformers have outperformed traditional convolution-based frameworks across various visual tasks, including, but not limited to, the segmentation of 3D medical images. To further advance this area, this study introduces the Coupled Swin Transformers and Multi-Apertures Networks (CSTA-Net), which integrates the outputs of each Swin Transformer with an Aperture Network. Each aperture network consists of a convolution and a fusion block for combining global and local feature maps. The proposed model has been tested on two independent datasets to show that fine details are delineated. The proposed architecture was trained on the Synapse multi-organ and ACDC datasets to conclude an average Dice score of 90.19±0.05 and 93.77±0.04, respectively. The code is available here: https://github.com/Siyavashshabani/CSTANet.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.