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Patch-based system for Classification of Breast Histology images using deep learning.

Kaushiki Roy1, Debapriya Banik1, Debotosh Bhattacharjee1

  • 1Department of Computer Science and Engineering, Jadavpur University, Kolkata-32, India.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 31, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a patch-based classifier (PBC) using Convolutional Neural Networks (CNNs) for automated breast histopathology image classification. The method achieves high accuracy in distinguishing between normal, benign, and malignant breast tissues.

Keywords:
Histopathological breast imagesconvolutional neural networksdeep learningmajority votingpatch-based classifier

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Accurate classification of histopathological breast images is crucial for diagnosis and treatment planning.
  • Limited availability of annotated datasets poses a challenge for training robust machine learning models.

Purpose of the Study:

  • To develop and evaluate an automated patch-based classifier (PBC) utilizing Convolutional Neural Networks (CNNs) for histopathological breast image classification.
  • To address the challenge of limited training data through patch extraction and data augmentation.

Main Methods:

  • Proposed a patch-based classifier (PBC) employing Convolutional Neural Networks (CNNs).
  • Implemented two classification modes: One Patch In One Decision (OPOD) and All Patches In One Decision (APOD).
  • Utilized patch extraction and augmentation techniques to enhance the training dataset size.

Main Results:

  • The OPOD mode achieved 77.4% patch-wise accuracy for 4 classes and 84.7% for 2 classes on a split test set.
  • The APOD mode demonstrated superior image-wise accuracy, reaching 90% for 4 classes and 92.5% for 2 classes on the split test set.
  • An accuracy of 87% was achieved on the hidden test dataset of the ICIAR-2018 dataset.

Conclusions:

  • The proposed patch-based classifier (PBC) effectively automates the classification of histopathological breast images.
  • The APOD mode, utilizing majority voting, provides a robust approach for image-level classification, outperforming the OPOD mode.
  • The developed method shows significant potential for improving diagnostic accuracy in breast cancer pathology.