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author:

Li, Zuoyong (Li, Zuoyong.) [1] | Xu, Shicheng (Xu, Shicheng.) [2] | Li, Wei (Li, Wei.) [3] | Xu, Qingjiang (Xu, Qingjiang.) [4] | Cai, Wenchao (Cai, Wenchao.) [5] | Wu, Xiang (Wu, Xiang.) [6]

Indexed by:

EI

Abstract:

Recently, the Reverse Distillation (RD) model has shown importance in medical image analysis, especially in anomaly detection. However, when detecting finer lesions in lung CT scans, there is a core challenge of maintaining consistent representation in the teacher–student network to reduce the false positive rate. To address this issue, we propose an anomaly detection method with learning Consistent Representations for Reverse Distillation (CR4RD) of intra-image and intra-batch. On the one hand, we introduce a hard global cosine similarity loss in the reverse skip connection distillation framework, which enhances the intra-image consistency of representations, enabling the model to more comprehensively evaluate the overall normality of CT scan images. On the other hand, we propose a unified regularization loss to achieve intra-batch consistency, guiding a uniform distance distribution within the same batch and consistency of distance distribution among samples within the batch. Finally, the teacher and trained student network will maintain consistent representations of normal regions and show significant differences in anomaly regions for the anomaly detection task. Experimental results on real lung CT scan datasets demonstrate that the proposed method is not only efficient but also superior in anomaly detection compared with several state-of-the-art methods. © 2025 Elsevier Ltd

Keyword:

Lung cancer

Community:

  • [ 1 ] [Li, Zuoyong]Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Big Data, Minjiang University, Fuzhou; 350121, China
  • [ 2 ] [Xu, Shicheng]College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou; 350002, China
  • [ 3 ] [Li, Wei]College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou; 350118, China
  • [ 4 ] [Xu, Qingjiang]Department of Urology, Fuzhou University Affiliated Provincial Hospital, Provincial Clinical Medical Colleges of Fujian Medical University, Fuzhou; 350001, China
  • [ 5 ] [Cai, Wenchao]Department of Urology, Fuzhou University Affiliated Provincial Hospital, Provincial Clinical Medical Colleges of Fujian Medical University, Fuzhou; 350001, China
  • [ 6 ] [Wu, Xiang]Department of Urology, Fuzhou University Affiliated Provincial Hospital, Provincial Clinical Medical Colleges of Fujian Medical University, Fuzhou; 350001, China

Reprint 's Address:

  • [cai, wenchao]department of urology, fuzhou university affiliated provincial hospital, provincial clinical medical colleges of fujian medical university, fuzhou; 350001, china;;

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Source :

Biomedical Signal Processing and Control

ISSN: 1746-8094

Year: 2025

Volume: 107

4 . 9 0 0

JCR@2023

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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