# Weighted multi-level deep learning analysis and framework for processing breast cancer WSIs

arXiv: https://arxiv.org/abs/2106.14708 · HTML: https://mlsanity.com/paper/2106.14708/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification](https://mlsanity.com/paper/2106.02864/) (2021)

## Built on by (most cited first, 1 of 1)

- [Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review](https://mlsanity.com/paper/2306.01546/) (2023)

## Abstract (arXiv)

Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and Computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSI), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSI based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%.