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A real-time computer-aided diagnosis method for hydatidiform mole recognition using deep neural network.

Chengze Zhu1, Pingge Hu1, Xingtong Wang1

  • 1Department of Automation, Tsinghua University, Beijing, 100084, China.

Computer Methods and Programs in Biomedicine
|April 1, 2023
PubMed
Summary

This study introduces a deep learning method for real-time recognition of hydatidiform mole (HM) lesions, improving diagnostic accuracy. The AI model accurately identifies HM hydrops lesions, aiding pathologists in diagnosis.

Keywords:
Computer-aided diagnosisDeep learningHydatidiform moleImage segmentationPathology

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Area of Science:

  • Pathology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Hydatidiform mole (HM) is a common gestational trophoblastic disease with malignant potential.
  • Histopathological examination is standard for HM diagnosis, but observer variability leads to misdiagnosis.
  • Accurate feature extraction is crucial for improving HM diagnostic accuracy and speed.

Purpose of the Study:

  • To develop a deep learning-based computer-aided diagnosis (CAD) method for real-time recognition of HM hydrops lesions.
  • To enhance the accuracy and efficiency of HM diagnosis by leveraging deep neural networks (DNNs).

Main Methods:

  • Proposed a hydrops lesion recognition module using DeepLabv3+ with a novel compound loss function and stepwise training.
  • Developed image mosaic and edge extension modules for real-time application with moving slides.
  • Employed DNNs for robust feature extraction and segmentation of HM slide images.

Main Results:

  • The developed method achieved a pixel-level IoU of 77.0% and lesion-level recall of 86.2%.
  • The edge extension module improved performance by up to 9.0% in lesion-level IoU.
  • The system processes images in real-time (82 ms/frame), displaying accurately labeled lesions.

Conclusions:

  • This is the first method using DNNs for HM lesion recognition, offering a robust diagnostic aid.
  • The CAD system provides powerful feature extraction and segmentation for auxiliary HM diagnosis.
  • The approach addresses challenges in HM slide image analysis, reducing diagnostic errors.