TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers

Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into medical image segmentation. However, a comprehens...

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Veröffentlicht in:	Medical image analysis 2024-10, Vol.97, p.103280, Article 103280
Hauptverfasser:	Chen, Jieneng, Mei, Jieru, Li, Xianhang, Lu, Yongyi, Yu, Qihang, Wei, Qingyue, Luo, Xiangde, Xie, Yutong, Adeli, Ehsan, Wang, Yan, Lungren, Matthew P., Zhang, Shaoting, Xing, Lei, Lu, Le, Yuille, Alan, Zhou, Yuyin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Humans Image Processing, Computer-Assisted - methods Medical image segmentation Neural Networks, Computer U-Net Vision Transformers
Online-Zugang:	Volltext
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