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Efficient deep learning model for mitosis detection using breast histopathology images.

Monjoy Saha¹, Chandan Chakraborty¹, Daniel Racoceanu²

¹School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal, India.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|February 8, 2018

Summary

This summary is machine-generated.

This study introduces a deep learning model for automated mitosis detection in breast cancer histology images. The model aids pathologists in grading tumors, improving accuracy and efficiency in cancer prognosis.

Keywords:

Breast cancer Convolution Deep neural network Handcrafted features Hematoxylin and eosin Mitosis

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Live Cell Imaging of Chromosome Segregation During Mitosis

Live Cell Imaging of Chromosome Segregation During Mitosis

Published on: March 14, 2018

Area of Science:

Pathology
Computer Science
Medical Imaging

Background:

Mitosis detection is crucial for breast cancer prognosis and grading.
Manual mitosis quantification in whole slide images (WSIs) is time-consuming and challenging.
Automated methods are needed to assist pathologists.

Purpose of the Study:

To develop a supervised deep learning model for accurate mitosis detection in breast histopathology WSIs.
To integrate handcrafted features with a deep learning architecture for enhanced performance.
To provide an efficient tool for breast cancer grading and prognosis.

Main Methods:

A deep learning architecture comprising convolutional layers, max-pooling, ReLU activation, and fully connected layers was employed.
Handcrafted features (morphological, textural, intensity) from prior challenges were integrated.
A dropout layer was used to prevent overfitting.

Main Results:

The proposed model achieved 92% precision, 88% recall, and 90% F-score.
The integration of deep learning and handcrafted features improved detection accuracy.
The model demonstrates potential for efficient and effective breast cancer grading.

Conclusions:

The developed model offers a promising automated solution for mitosis detection in breast cancer WSIs.
This tool can serve as an effective second opinion for pathologists, enhancing cancer grading.
The model can contribute to a better understanding of breast cancer stage and genesis.