Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
This work addresses the challenge of identifying malignant cells in breast cancer diagnosis, which is critical for improving patient outcomes, but it appears incremental as it builds on existing architectures with a specific modification.
The paper tackles the problem of discriminating Invasive Ductal Carcinoma cells in histopathology images by proposing a model based on the Inception architecture with a multi-level batch normalization module, achieving a balanced accuracy of 0.89 and an F1 score of 0.90, surpassing recent state-of-the-art methods on a public dataset.
Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide. Imaging techniques such as the breast cancer pathology helps in the diagnosis and monitoring of the disease. However identification of malignant cells can be challenging given the high heterogeneity in tissue absorbotion from staining agents. In this work, we present a novel approach for Invasive Ductal Carcinoma (IDC) cells discrimination in histopathology slides. We propose a model derived from the Inception architecture, proposing a multi-level batch normalization module between each convolutional steps. This module was used as a base block for the feature extraction in a CNN architecture. We used the open IDC dataset in which we obtained a balanced accuracy of 0.89 and an F1 score of 0.90, thus surpassing recent state of the art classification algorithms tested on this public dataset.