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An end-to-end breast tumour classification model using context-based patch modelling - A BiLSTM approach for image

Suvidha Tripathi¹, Satish Kumar Singh¹, Hwee Kuan Lee²

¹Department of Information Technology, Indian Institute of Information Technology Allahabad, Devghat, Jhalwa, Prayagraj 211015, India.

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

|December 19, 2020

Summary

This summary is machine-generated.

This study introduces a novel deep learning approach for analyzing whole slide images (WSIs) in histopathology. By integrating spatial relationships and using BiLSTMs, the method achieves high accuracy in tumor classification without resizing images.

Keywords:

BACH BiLSTMs Breast tumours classification Classification Computational pathology Convolutional neural networks Deep learning Histopathology ICIAR LSTM Microscopy images RNN Whole slide images

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Area of Science:

Computational pathology
Digital histopathology
Machine learning in medicine

Background:

Whole Slide Images (WSIs) are large, posing computational challenges for direct machine learning analysis.
Current patch-based methods often overlook spatial relationships between image patches.
Histopathology image analysis requires methods that can handle variable image dimensions.

Purpose of the Study:

To develop a deep learning model that integrates spatial relationships and feature correlations for WSI analysis.
To address the limitations of patch-based modeling in capturing WSI context.
To enable accurate classification of tumor regions from WSIs regardless of their size.

Main Methods:

Utilized BiLSTMs (Bidirectional Long Short-Term Memory networks) to model forward and backward contextual relationships among image patches.
Incorporated spatial continuity by exploring different patch sampling techniques.
Employed Convolutional Neural Network (CNN) features extracted from patches.
Developed an end-to-end image classification network capable of processing variable-sized WSI tumor regions.

Main Results:

Achieved 90% accuracy on the microscopy image dataset from the ICIAR BACH Challenge 2018, outperforming top competitors.
Attained 84% accuracy on the WSI tumor region dataset, surpassing state-of-the-art networks like ResNet, DenseNet, and InceptionV3.
Demonstrated that BiLSTMs with CNN features effectively model patches for end-to-end image classification.
Successfully classified variable-dimension WSI tumor regions without resizing, preserving resolution details.

Conclusions:

The proposed BiLSTM-based approach effectively models spatial and feature-based correlations in histopathology images.
This method overcomes computational constraints and the loss of contextual information inherent in traditional patch-based analyses.
The technique offers a robust solution for accurate and efficient classification of WSI tumor regions, independent of image size.