Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images
Summary
Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images is a scholarly article[1].
Key Facts
Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images. Retrieved May 24, 2026, from https://4ort.xyz/entity/attention-vgg16-unet-a-novel-deep-learning-approach-for-automatic-segmentation-of-the-median-nerve-in-ultrasound-images
MLA“Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/attention-vgg16-unet-a-novel-deep-learning-approach-for-automatic-segmentation-of-the-median-nerve-in-ultrasound-images.
BibTeX@misc{4ortxyz_attention-vgg16-unet-a-novel-deep-learning-approach-for-automatic-segmentation-of-the-median-nerve-in-ultrasound-images_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images}}, year = {2026}, url = {https://4ort.xyz/entity/attention-vgg16-unet-a-novel-deep-learning-approach-for-automatic-segmentation-of-the-median-nerve-in-ultrasound-images}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images — https://4ort.xyz/entity/attention-vgg16-unet-a-novel-deep-learning-approach-for-automatic-segmentation-of-the-median-nerve-in-ultrasound-images (retrieved 2026-05-24)
