Roto-translation equivariant convolutional networks: Application to histopathology image analysis.

Area of Science:

• Medical Image Analysis
• Computational Pathology
• Machine Learning

Background:

• Rotation-invariance is crucial for machine learning models in medical image analysis, especially in computational pathology.
• Conventional methods like data augmentation offer limited robustness for rotation invariance in trained models.
• Histopathology image analysis requires models that do not capture arbitrary global orientation of tissue samples.

Purpose of the Study:

• To propose a novel framework for achieving translation and rotation equivariance in convolutional networks.
• To encode the geometric structure of the special Euclidean motion group SE(2) into convolutional layers.
• To enhance the performance of machine learning models in histopathology image analysis tasks.

Main Methods:

• Introduction of SE(2)-group convolution layers into convolutional networks.
• Development of a framework that learns feature representations with a discretized orientation dimension.
• Evaluation of the framework on mitosis detection, nuclei segmentation, and tumor detection tasks.

Main Results:

• The proposed SE(2)-group convolution framework yields translation and rotation equivariance.
• Models trained with the framework demonstrate improved performance across multiple histopathology image analysis tasks.
• Consistent performance increases were observed compared to conventional approaches.

Conclusions:

• The SE(2)-group convolution framework offers a robust solution for achieving rotation equivariance in medical image analysis.
• This approach enhances the reliability and performance of machine learning models in computational pathology.
• The framework provides a significant advancement over traditional data augmentation techniques for rotation invariance.