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Automated brain histology classification using machine learning.

Justin Ker1, Yeqi Bai2, Hwei Yee Lee3

  • 1Department of Neurosurgery, National Neuroscience Institute, 308433, Singapore.

Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia
|June 4, 2019
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Summary
This summary is machine-generated.

This study introduces an automated method using artificial intelligence to classify brain and breast tumors from histology slides. Transfer learning significantly improved accuracy for breast tumor classification, aiding pathologists and potentially assisting with rare tumor diagnosis.

Keywords:
Automated medical diagnosisBrain histologyConvolutional neural networksGlioma histologyMachine learning

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Digital histopathology

Background:

  • Brain and breast tumors are major global health concerns, necessitating accurate and timely histological diagnosis.
  • Manual histopathology slide review is time-consuming and labor-intensive, impacting patient care timelines.
  • Automated analysis of histology slides offers a potential solution to improve diagnostic efficiency.

Purpose of the Study:

  • To develop and evaluate an automated system for classifying brain and breast histology slides using a convolutional neural network (CNN).
  • To investigate the efficacy of transfer learning across different tissue types for improving CNN performance.
  • To assess the potential of AI in assisting pathologists with slide triage and diagnosis, especially for rare tumors.

Main Methods:

  • Utilized Google Inception V3 convolutional neural network (CNN) for automated classification of histology slides.
  • Developed a dataset comprising brain histology images from a hospital and public breast histology image data.
  • Employed transfer learning by pre-training the CNN on brain tumor classification before applying it to breast tumor classification.

Main Results:

  • Achieved successful automated classification of brain histology specimens into normal, low-grade glioma (LGG), and high-grade glioma (HGG).
  • Demonstrated significant improvement in breast tumor classification accuracy via transfer learning, with F1 score increasing from 0.547 to 0.913.
  • Validated the benefit of cross-tissue transfer learning for enhancing CNN performance, particularly with limited training data.

Conclusions:

  • The proposed automated CNN method can effectively classify brain and breast histology slides, supporting pathologists in diagnosis.
  • Transfer learning across tissue types is a viable strategy to boost CNN performance, especially in scenarios with scarce data.
  • This AI-driven approach has the potential to expedite medical care and improve diagnostic accuracy for various tumor types.