相关实验视频

Updated: Sep 9, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

491

通过多个部分标记数据集进行通用腹部多器官细分

Xiang Li1, Faming Fang2, Liyan Ma3

  • 1School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
|September 3, 2025
PubMed
概括

一般腹部多器官细分 (GAMOS) 框架使用扩散模型和自蒸改进了医疗图像细分,以更好地处理部分标签和未标签数据. 它有效地减少了域名转移,增强了跨不同数据集的概括性.

关键词:
腹部多器官细分扩散模型图像细分

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.8K

相关概念视频

Abdominal Regions and Quadrants01:19

Abdominal Regions and Quadrants

To promote clear communication, for instance, about the location of a patient's abdominal pain or a suspicious mass, anatomists and clinicians typically use imaginary lines to categorize the abdominopelvic cavity into either four quadrants or nine regions to identify organs in the cavity.
The simpler quadrants approach, which is more commonly used in medicine, subdivides the cavity with one horizontal and one vertical line that intersects at the patient's umbilicus (navel). The four...
12.0K