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Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images.

Asaad Anaam1, Mugahed A Al-Antari2, Jamil Hussain3

  • 1Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama 700-8530, Japan.

Diagnostics (Basel, Switzerland)
|May 16, 2023
PubMed
Summary

A new deep active learning approach automates the identification of Human Epithelial Type 2 (HEp-2) mitotic cells for anti-nuclear antibodies (ANAs) testing. This computer-aided diagnosis system enhances accuracy and throughput in detecting connective tissue diseases (CTD).

Keywords:
HEp-2 mitotic cell detectionautomatic data annotationcomputer-aided detection (CAD)deep active learning (DAL)medical HEp-2 specimen images

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Area of Science:

  • Medical Imaging Analysis
  • Computational Pathology
  • Artificial Intelligence in Diagnostics

Background:

  • Accurate identification of Human Epithelial Type 2 (HEp-2) mitotic cells is critical for anti-nuclear antibodies (ANAs) testing in diagnosing connective tissue diseases (CTD).
  • Current manual screening methods for ANAs are labor-intensive, subjective, and have low throughput, necessitating automated solutions.
  • Computer-aided diagnosis (CAD) systems are needed to improve the efficiency and reliability of HEp-2 cell analysis.

Purpose of the Study:

  • To develop a deep active learning (DAL) framework for automated detection of mitotic cells in HEp-2 specimen images.
  • To overcome the challenge of limited labeled cell data through an efficient active learning strategy.
  • To directly identify mitotic cells in whole microscopic images, eliminating the need for a separate segmentation step.

Main Methods:

  • A deep active learning (DAL) approach was implemented to train deep learning detectors for mitotic cell identification.
  • The YOLO and Faster R-CNN deep learning models were adapted to directly detect mitotic cells in HEp-2 images.
  • The framework was evaluated on the I3A Task-2 dataset using 5-fold cross-validation, with iterative data labeling rounds.

Main Results:

  • The YOLO predictor achieved high performance with average scores of 90.011% recall, 88.307% precision, and 81.531% mAP.
  • The Faster R-CNN predictor demonstrated strong results with average scores of 86.986% recall, 85.282% precision, and 78.506% mAP.
  • The DAL method significantly improved prediction accuracy by enhancing data annotation quality over four labeling rounds.

Conclusions:

  • The proposed DAL framework effectively automates the detection of HEp-2 mitotic cells, addressing the limitations of manual screening.
  • The system demonstrates practical applicability for assisting medical professionals in rapid and accurate diagnostic decisions.
  • This approach enhances the throughput and objectivity of ANAs testing, potentially improving the diagnosis of connective tissue diseases.