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Ruru ZHANG, Haihong E, Lifei YUAN, et al., “FGM-SPCL: Open-Set Recognition Network for Medical Images based on Fine-Grained data Mixture and Spatial Position Constraint Loss,” Chinese Journal of Electronics, vol. x, no. x, article no. , xxxx doi: 10.23919/cje.2023.00.081
Citation: Ruru ZHANG, Haihong E, Lifei YUAN, et al., “FGM-SPCL: Open-Set Recognition Network for Medical Images based on Fine-Grained data Mixture and Spatial Position Constraint Loss,” Chinese Journal of Electronics, vol. x, no. x, article no. , xxxx doi: 10.23919/cje.2023.00.081

FGM-SPCL: Open-Set Recognition Network for Medical Images based on Fine-Grained data Mixture and Spatial Position Constraint Loss

doi: 10.23919/cje.2023.00.081
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  • Author Bio:

    Ruru ZHANG was born in 1992. At present, she is studying at Beijing University of Posts and Telecommunications for a doctorate in computer science and technology. Her interests include artificial intelligence and medical image processing. (Email: zrr@bupt.edu.cn)

    Haihong E was born in 1982. She graduated from Beijing University of Posts and Telecommunications with a doctor’s degree in computer science and technology. At present, she is a professor at Beijing University of Posts and Telecommunications. She has been engaged in research and teaching in the fields of big data, artificial intelligence and cloud native services for a long time. (Email: ehaihong@bupt.edu.cn)

    Lifei YUAN was born in 1986. He graduated from Tianjin Medical University with a master’s degree in ophthalmology. His main research direction is fundus disease. (Email: yuanlifei2341@163.com)

    Yanhui WANG was born in 1981. She graduated from Tianjin Medical University with a master’s degree in ophthalmology. Her main research direction is fundus disease. (Email: yanhuiwang1981@163.com)

    Lifei WANG (co-corresponding authorr) was born in August 1976. She graduated from Sun Yat-Sen University majoring in Ophthalmology with a doctorate degree. She is currently Secretary of the Party Committee of Hebei Provincial Eye Hospital. Her main research direction is fundus disease. (Email: wlfhb@126.com)

    Meina SONG (corresponding author) was born in 1974. She graduated from Beijing University of Posts and Telecommunications with a doctor’s degree in computer science and technology. At present, she is a professor of Beijing University of Posts and Telecommunications and director of the Information Network Engineering Research Center of the Ministry of Education. His main research direction is artificial intelligence and its application in the fields of finance and medicine. (Email: mnsong@bupt.edu.cn)

  • Corresponding author: Email: mnsong@bupt.edu.cn
  • Received Date: 2022-03-22
  • Accepted Date: 2022-03-22
  • Available Online: 2022-03-22
  • The current intelligent auxiliary diagnosis models all follow the closed-set recognition setting. After the model is deployed online, the input data is often not completely controlled. Diagnosing an untrained disease as a known category would lead to serious medical malpractice. Therefore, realizing the open set recognition is significant to the safe operation of the intelligent auxiliary diagnosis model. Currently, most open-set recognition models are studied for natural images, and it is very challenging to obtain clear and concise decision boundaries be-tween known and unknown classes when applied to fine-grained medical images. Therefore, we propose an Open-Set Recognition Network for Medical Images based on Fine-Grained data Mixture and Spatial Position Constraint Loss (FGM-SPCL) in this work. First, considering the fine graininess of medical images and the diversity of unknown samples, we propose a fine-grained data Mixture (FGM) method to simulate unknown data by performing a mixing operation on known data to expand the coverage of unknown data difficulty levels. Secondly, in order to obtain a concise and clear decision boundary, we propose a Spatial Position Constraint Loss (SPCL) to control the position distribution of prototypes and samples in the feature space and maximize the distance between known classes and unknown classes. Finally, we validate on a private ophthalmic OCT dataset, and extensive experiments and analyses demonstrate that FGM-SPCL outperforms state-of-the-art models.
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