Deep-learning-based real-time and automatic target-to-background ratio calculation in fluorescence endoscopy for cancer detection and localization

HIGHLIGHTS

• who: Yang Jiang et al. from the Department of Mechanical Engineering, University of Washington, Seattle, WA, USA have published the Article: Deep-Learning-Based Real-Time and Automatic Target-to-Background Ratio Calculation in Fluorescence Endoscopy for Cancer Detection and Localization, in the Journal: Diagnostics 2022, 12, x FOR PEER REVIEW of /2022/
• what: In this study, 2 different architectures, UNet and BiSeNet, were evaluated for fluorescence target segmentation. In this study, a deep-learning CAD pipeline was developed to achieve real-time and the models made their decisions, a grad-CAM was applied. This . . .
  

   

  

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