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Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

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Abstract

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

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Acknowledgements

This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research and the Patrick J. McGovern Foundation Award.

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Authors and Affiliations

  1. Department of Computer Science, Johns Hopkins University, Baltimore, 21218, USA

    Yu-Cheng Chou, Bowen Li, Alan Yuille & Zongwei Zhou

  2. Computer Vision Lab, ETH Zürich, Zürich, 8001, Switzerland

    Deng-Ping Fan

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Correspondence to Zongwei Zhou.

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Deng-Ping Fan is an Associate Editor for Machine Intelligence Research and was not involved in the editorial review, or the decision to publish this article. All authors declare that there are no other competing interests.

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Yu-Cheng Chou received the B. Sc. degree in software engineering at the School of Computer Science, Wuhan University, China in 2022. He is currently a Ph.D. degree candidate at Johns Hopkins University, supervised by Zongwei Zhou and Prof. Alan Yuille.

His research interests include medical imaging, causality, and computer vision. His research focuses on developing novel methodologies to accurately detect lesions and exploring explainability through causality for computer-aided diagnosis and surgery.

Bowen Li received the B. Sc. degree in automation at Tsinghua University, China in 2013, and the M. Sc. degree in computer science at Johns Hopkins University, USA in 2019. He is currently a Ph.D. degree candidate at Johns Hopkins University, supervised by Zongwei Zhou and Prof. Alan Yuille.

His research interest is computer-aided diagnosis with ultrasound and CT.

Deng-Ping Fan received the Ph.D. degree from Nankai University, China in 2019. He joined the Inception Institute of Artificial Intelligence (HAI), UAE in 2019. He has published about 50 top journal and conference papers such as TPAMI, IJCV, TIP, TNNLS, TMI, CVPR, ICCV, ECCV, IJCAI, etc. He won the Best Paper Finalist Award at IEEE CVPR 2019, and the Best Paper Award Nominee at IEEE CVPR 2020. He was recognized as the CVPR 2019 outstanding reviewer with a special mention award, the CVPR 2020 outstanding reviewer, the ECCV 2020 high-quality reviewer, and the CVPR 2021 outstanding reviewer. He served as a program committee board (PCB) member of IJCAI 2022–2024, a senior program committee (SPC) member of IJCAI 2021, a program committee member (PC) of CAD&CG 2021, a committee member of China Society of Image and Graphics (CSIG), area chair in NeurlPS 2021 Datasets and Benchmarks Track, area chair in MICCAI2020 Workshop.

His research interests include computer vision, deep learning, and saliency detection.

Alan Yuille received the B. Sc. degree in mathematics from the University of Cambridge, UK in 1976, and the Ph.D. degree in theoretical physics, supervised by Prof. S.W. Hawking, was approved in 1981. He was a research scientist in the Artificial Intelligence Laboratory at MIT and the Division of Applied Sciences at Harvard University, USA from 1982 to 1988. He served as an assistant and associate professor at Harvard until 1996. He was a senior research scientist at the Smith-Kettlewell Eye Research Institute, USA from 1996 to 2002. He was a full professor of statistics at the University of California, Los Angeles, as a full professor with joint appointments in computer science, psychiatry, and psychology. He moved to Johns Hopkins University, USA in January 2016.

His research interests include computational models of vision, mathematical models of cognition, medical image analysis, artificial inteUigence and neural networks.

Zongwei Zhou received the Ph.D. degree in biomedical informatics at Arizona State University, USA in 2021. He is currently a postdoctoral researcher at Johns Hopkins University, USA. He received the AMIA Doctoral Dissertation Award in 2022, the Elsevier-MedIA Best Paper Award in 2020, and the MICCAI Young Scientist Award in 2019. In addition to five U.S. patents, he has published over 30 peer-reviewed journal/conference articles, two of which have been ranked among the most popular articles in IEEE TMI and the highest-cited article in EJNMMI Research. He was named the top 2% of Scientists released by Stanford University in 2022 and 2023.

His research interest is developing novel methods to reduce the annotation efforts for computer-aided detection and diagnosis.

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Chou, YC., Li, B., Fan, DP. et al. Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data. Mach. Intell. Res. 21, 318–330 (2024). https://doi.org/10.1007/s11633-023-1380-5

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