SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information
Summary
SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information is a scholarly article[1].
Key Facts
SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information. Retrieved May 24, 2026, from https://4ort.xyz/entity/swint-unet-hybrid-architecture-for-medical-image-segmentation-based-on-swin-transformer-block-and-dual-scale-information
MLA“SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/swint-unet-hybrid-architecture-for-medical-image-segmentation-based-on-swin-transformer-block-and-dual-scale-information.
BibTeX@misc{4ortxyz_swint-unet-hybrid-architecture-for-medical-image-segmentation-based-on-swin-transformer-block-and-dual-scale-information_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information}}, year = {2026}, url = {https://4ort.xyz/entity/swint-unet-hybrid-architecture-for-medical-image-segmentation-based-on-swin-transformer-block-and-dual-scale-information}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information — https://4ort.xyz/entity/swint-unet-hybrid-architecture-for-medical-image-segmentation-based-on-swin-transformer-block-and-dual-scale-information (retrieved 2026-05-24)
