UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-at...

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Veröffentlicht in:	arXiv.org 2023-09
Hauptverfasser:	Yu, Xin, Yang, Qi, Zhou, Yinchi, Cai, Leon Y, Gao, Riqiang, Lee, Ho Hin, Li, Thomas, Bao, Shunxing, Xu, Zhoubing, Lasko, Thomas A, Abramson, Richard G, Zhang, Zizhao, Huo, Yuankai, Landman, Bennett A, Tang, Yucheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain Coders Computer vision Datasets Image analysis Image segmentation Kidneys Learning Medical imaging Performance evaluation Representations State-of-the-art reviews Structural hierarchy
Online-Zugang:	Volltext
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