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Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data | |
Yu-Cheng Chou1; Bowen Li1; Deng-Ping Fan2; Alan Yuille1; Zongwei Zhou1![]() | |
发表期刊 | Machine Intelligence Research
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ISSN | 2731-538X |
2024 | |
卷号 | 21期号:2页码:318-330 |
摘要 | Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans; Both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data, involving either temporary or spatial dimensions. In this paper, we develop a new annotation strategy, termed Drag&Drop, which simplifies the annotation process to drag and drop. This annotation strategy is more efficient, particularly for temporal and volumetric imaging, than other types of weak annotations, such as per-pixel, bounding boxes, scribbles, ellipses and points. Furthermore, to exploit our Drag&Drop annotations, we develop a novel weakly supervised learning method based on the watershed algorithm. Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and, more importantly, achieves similar performance to that trained on detailed per-pixel annotations. Interestingly, we find that, with limited resources, allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images. In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities. Project Page: https://github.com/johnson111788/Drag-Drop. |
关键词 | Weak annotation, detection, localization, segmentation, colonoscopy, abdomen |
DOI | 10.1007/s11633-023-1380-5 |
语种 | 英语 |
七大方向——子方向分类 | 其他 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
中文导读 | https://mp.weixin.qq.com/s/2Rh1bJL6lmd2Mix6iDpEaQ |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56041 |
专题 | 学术期刊_Machine Intelligence Research |
作者单位 | 1.Department of Computer Science, Johns Hopkins University, Baltimore 21218, USA 2.Computer Vision Lab, ETH Zürich, Zürich 8001, Switzerland |
推荐引用方式 GB/T 7714 | Yu-Cheng Chou,Bowen Li,Deng-Ping Fan,et al. Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data[J]. Machine Intelligence Research,2024,21(2):318-330. |
APA | Yu-Cheng Chou,Bowen Li,Deng-Ping Fan,Alan Yuille,&Zongwei Zhou.(2024).Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data.Machine Intelligence Research,21(2),318-330. |
MLA | Yu-Cheng Chou,et al."Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data".Machine Intelligence Research 21.2(2024):318-330. |
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MIR-2023-07-135.pdf(4008KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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