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Updated: Jul 23, 2025

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Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection.

Ya-Han Chang1, Meng-Ying Lin2, Ming-Tsung Hsieh2

  • 1Department of Computer Science and EngineeringNational Chung Hsing University Taichung 402202 Taiwan.

IEEE Journal of Translational Engineering in Health and Medicine
|July 19, 2023
PubMed
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This summary is machine-generated.

Detecting common bile duct (CBD) stones using CT scans is challenging. A new deep learning model, MFADNet, accurately locates these stones with image-level labels, aiding physician diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Common bile duct (CBD) stones pose life-threatening risks.
  • Detecting small, distal CBD stones in CT scans is difficult.

Purpose of the Study:

  • To develop a weakly-supervised deep learning method for detecting CBD stones in CT scans.
  • To reduce the burden of detailed labeling for medical professionals.

Main Methods:

  • Proposed a multiple field-of-view based attention driven network (MFADNet).
  • Employed a multiple field-of-view encoder and an attention-driven decoder with a classification network.
  • Utilized four losses (foreground, background, consistency, classification) for end-to-end training.

Main Results:

Keywords:
Common bile duct (CBD) stone detectioncholedocholithiasisdeep learningobject detectionweakly-supervised learning

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  • MFADNet accurately classifies and locates CBD stones.
  • Demonstrated superior performance compared to state-of-the-art weakly-supervised methods.

Conclusions:

  • MFADNet offers a novel, weakly-supervised approach for CBD stone detection from CT scans.
  • The method assists physicians in the automatic diagnosis of CBD stone-related diseases.