The transition module: a method for preventing overfitting in convolutional neural networks

Abstract

Digital pathology has advanced substantially over the last decade with the adoption of slide scanners in pathology labs. The use of digital slides to analyse diseases at the microscopic level is both cost-effective and efficient. Identifying complex tumour patterns in digital slides is a challenging problem but holds significant importance for tumour burden assessment, grading and many other pathological assessments in cancer research. The use of convolutional neural networks (CNNs) to analyse such complex images has been well adopted in digital pathology. However, in recent years, the architecture of CNNs has altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified ‘transition’ module which encourages generalisation in a deep learning framework with few training samples. In the transition module, filters of varying sizes are used to encourage class-specific filters at multiple spatial resolutions followed by global average pooling. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumours in two independent data-sets of scanned histology sections; the inclusion of the transition module in these CNNs improved performance.

Keywords:

• Convolutional neural networks
• histology
• inception
• breast tumour
• overfitting

Acknowledgements

We gratefully acknowledge the support of NVidia Corporation with the donation of GPUs used for this research.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work has been supported by grants from the Canadian Breast Cancer Foundation, Canadian Cancer Society [grant number 703006] and the National Cancer Institute of the National Institutes of Health [grant number U24CA199374-01], and also supported by NVidia Corporation with the donation of GPUs used for this research.