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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
Source PublicationMachine Intelligence Research

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:

KeywordWeak annotation, detection, localization, segmentation, colonoscopy, abdomen
Sub direction classification其他
planning direction of the national heavy laboratory其他
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Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.Department of Computer Science, Johns Hopkins University, Baltimore 21218, USA
2.Computer Vision Lab, ETH Zürich, Zürich 8001, Switzerland
Recommended Citation
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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