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A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color

Xu Xu1, Congsheng Li1, Xingjie Lan2

  • 1China Academy of Information and Communications Technology, No.52, Huayuan bei Road, 100191, Beijing, China.

Journal of Digital Imaging
|May 25, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning network, FISH-Net, accurately identifies circulating abnormal cells (CACs) using 4-color fluorescence in situ hybridization (FISH) images. This AI tool significantly improves cancer diagnosis efficiency and accuracy, outperforming traditional methods.

Keywords:
Circulating genetically abnormal cellDeep learningFluorescence in situ hybridizationObject detection

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Oncology

Background:

  • Circulating abnormal cells (CACs) are vital biomarkers for cancer diagnosis and prognosis, offering a safe, cost-effective, and repeatable method.
  • Current identification relies on 4-color fluorescence in situ hybridization (FISH) technology, which faces challenges due to signal morphology and staining intensity variations.

Purpose of the Study:

  • To develop a deep learning network (FISH-Net) for accurate and efficient identification of CACs from 4-color FISH images.
  • To address limitations in current CACs identification methods, including signal variability and noise interference.

Main Methods:

  • Developed a lightweight object detection network incorporating signal size statistics for improved detection rates.
  • Utilized a rotated Gaussian heatmap with a covariance matrix to standardize signals of varying morphologies.
  • Implemented a heatmap refinement model to mitigate fluorescent noise and an online repetitive training strategy for enhanced feature extraction from challenging signals.

Main Results:

  • FISH-Net achieved >96% precision and >98% sensitivity in fluorescent signal detection.
  • Clinical validation across 10 centers with 853 patients demonstrated 97.18% sensitivity for CACs identification.
  • FISH-Net, with 2.24 M parameters, is significantly more lightweight and approximately 800 times faster than a pathologist.

Conclusions:

  • FISH-Net is a robust and lightweight deep learning model for CACs identification.
  • The network enhances review accuracy, reviewer efficiency, and reduces turnaround time in clinical settings.
  • FISH-Net shows significant potential to advance cancer diagnostics through improved biomarker analysis.