Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network

TL;DR

This paper introduces a novel CNN-based approach with data augmentation and attention mechanisms to improve detection of lymph node metastases in breast cancer from digital pathology images, achieving high accuracy.

Contribution

It proposes a new data augmentation method called Random Center Cropping and reduces network downsampling to enhance small resolution image analysis in CNNs.

Findings

Achieved 97.96% accuracy on RPCam datasets.

Attained 99.68% AUC, demonstrating high diagnostic performance.

Enhanced CNN performance with attention and feature fusion mechanisms.

Abstract

In recent years, advances in the development of whole-slide images have laid a foundation for the utilization of digital images in pathology. With the assistance of computer images analysis that automatically identifies tissue or cell types, they have greatly improved the histopathologic interpretation and diagnosis accuracy. In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer. Unlike traditional image cropping methods that are only suitable for large resolution images, we propose a novel data augmentation method named Random Center Cropping (RCC) to facilitate small resolution images. RCC enriches the datasets while retaining the image resolution and the center area of images. In addition, we reduce the downsampling scale of the network to further facilitate small resolution images better. Moreover, Attention and Feature Fusion (FF) mechanisms are employed to improve the semantic information of images. Experiments demonstrate that our methods boost performances of basic CNN architectures. And the best-performed method achieves an accuracy of 97.96% and an AUC of 99.68% on RPCam datasets, respectively.