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

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外文摘要：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 between known and unknown classes when applied to fine-grained medical images.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.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.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.We validate on a private ophthalmic OCT dataset,and extensive experiments and analyses demonstrate that FGM-SPCL outperforms state-of-the-art models.

外文关键词：

Few-shot class-incremental learningEmbedding augmentationClassifier adaptationImage clas-sification

作者：

Ruru ZHANG、Haihong E、Lifei YUAN、Yanhui WANG、Lifei WANG、Meina SONG

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作者单位：

School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China

Engineering Research Center of Information Networks,Ministry of Education,Beijing 100876,China

Hebei Eye Hospital,Xingtai 054001,China

基金：

National Natural Science Foundation of ChinaBeijing Natural Science FoundationKey Research and Development Program of HebeiEngineering Research Center of Information Networks,Ministry of Education

项目编号：

62176026M2200922377775D

出版年：

2024

DOI：

10.23919/cje.2023.00.081

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(4)