# Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images

## TL;DR

This paper introduces a novel CNN model with multi-level batch normalization for improved discrimination of invasive ductal carcinoma cells in histopathology images, achieving state-of-the-art accuracy on a public dataset.

## Contribution

The work proposes a new multi-level batch normalization module within an Inception-based CNN for better feature extraction in IDC cell classification.

## Key findings

- Achieved 0.89 balanced accuracy on IDC dataset
- F1 score of 0.90 surpassing recent methods
- Demonstrated effectiveness of multi-level batch normalization

## Abstract

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.