Fast object detection leveraging global feature fusion in boundary-aware convolutional networks

Weiming Fan, Jiahui Yu, Zhaojie Ju*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Endoscopy, a pervasive instrument for the diagnosis and treatment of hollow anatomical structures, conventionally necessitates the arduous manual scrutiny of seasoned medical experts. Nevertheless, the recent strides in deep learning technologies proffer novel avenues for research, endowing it with the potential for amplified robustness and precision, accompanied by the pledge of cost abatement in detection procedures, while simultaneously providing substantial assistance to clinical practitioners. Within this investigation, we usher in an innovative technique for the identification of anomalies in endoscopic imagery, christened as Context-enhanced Feature Fusion with Boundary-aware Convolution (GFFBAC). We employ the Context-enhanced Feature Fusion (CEFF) methodology, underpinned by Convolutional Neural Networks (CNNs), to establish equilibrium amidst the tiers of the feature pyramids. These intricately harnessed features are subsequently amalgamated into the Boundary-aware Convolution (BAC) module to reinforce both the faculties of localization and classification. A thorough exploration conducted across three disparate datasets elucidates that the proposition not only surpasses its contemporaries in object detection performance but also yields detection boxes of heightened precision.

Original languageEnglish
Article number53
Number of pages18
JournalInformation (Switzerland)
Volume15
Issue number1
DOIs
Publication statusPublished - 17 Jan 2024

Keywords

  • computer vision
  • deep learning
  • endoscopy
  • object detection
  • polyps

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