AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking-Reference-Cited by-同舟云学术

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AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking

Published:2024-10 Issue: Volume:97 Page:103285
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Li Wenxuan,Qu Chongyu^ORCID,Chen Xiaoxi,Bassi Pedro R.A.S.^ORCID,Shi Yijia,Lai Yuxiang^ORCID,Yu Qian^ORCID,Xue Huimin^ORCID,Chen Yixiong^ORCID,Lin Xiaorui,Tang Yutong,Cao Yining,Han Haoqi^ORCID,Zhang Zheyuan,Liu Jiawei^ORCID,Zhang Tiezheng^ORCID,Ma Yujiu^ORCID,Wang Jincheng^ORCID,Zhang Guang,Yuille Alan,Zhou Zongwei^ORCID

Publisher

Elsevier BV

Reference72 articles.

1. The medical segmentation decathlon;Antonelli,2021

2. Sequential modeling enables scalable learning for large vision models;Bai,2023

3. The liver tumor segmentation benchmark (LITS);Bilic,2019

4. Merlin: A vision language foundation model for 3D computed tomography;Blankemeier,2024

5. On the opportunities and risks of foundation models;Bommasani,2021

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