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Related Experiment Video

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Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer

Parisa Gifani1, Ahmad Shalbaf2,3

  • 1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Journal of Medical Signals and Sensors
|March 21, 2024
PubMed
Summary

This study introduces an automated method using convolutional neural networks (CNNs) for Gleason grading of prostate cancer. The developed system achieves high accuracy, offering objective and reproducible cancer grading.

Keywords:
Convolutional neural networkGleason gradingprostate cancertransfer learning

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Area of Science:

  • Oncology
  • Digital Pathology
  • Artificial Intelligence in Medicine

Background:

  • The Gleason grading system is crucial for predicting prostate cancer aggressiveness and guiding treatment decisions.
  • Current Gleason grading relies on expert pathologists, which is time-consuming, subjective, and prone to variability.
  • Automating Gleason grading can improve efficiency, objectivity, and reproducibility in prostate cancer assessment.

Purpose of the Study:

  • To develop and evaluate an automated methodology for Gleason grading of prostate cancer using transfer learning and CNNs.
  • To compare the performance of various pretrained CNN architectures for this task.
  • To provide an objective and reproducible alternative to manual Gleason grading.

Main Methods:

  • Fine-tuning fifteen pretrained convolutional neural network (CNN) architectures on a dataset of prostate carcinoma tissue microarray (TMA) images.
  • Utilizing pixel-wise annotations from six pathologists, with a majority vote applied to establish unified labels for training and validation.
  • Evaluating the performance of CNN models on TMA images from 244 patients.

Main Results:

  • The NasNetLarge architecture demonstrated superior performance among the evaluated models.
  • NasNetLarge achieved an accuracy of 0.93 and an area under the curve (AUC) of 0.98 in classifying prostate TMA images.
  • The results indicate the potential of automated CNN-based grading for prostate cancer.

Conclusions:

  • The proposed automated methodology effectively categorizes prostate cancer stages.
  • The CNN-based approach offers more objective and reproducible results compared to traditional pathologist-based grading.
  • This technology has the potential to assist pathologists and improve the accuracy of prostate cancer assessment.