Feasibility of Universal Anomaly Detection Without Knowing the Abnormality in Medical Images - researchr publication authors

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Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Joseph T. Roland, Ken S. Lau, Qi Liu 0024, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo. Feasibility of Universal Anomaly Detection Without Knowing the Abnormality in Medical Images. In Zhiyun Xue, Sameer K. Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang, editors, Medical Image Learning with Limited and Noisy Data - Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. Volume 14307 of Lecture Notes in Computer Science, pages 82-92, Springer, 2023. [doi]

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