Weighted multi-level deep learning analysis and framework for processing
breast cancer WSIs
- URL: http://arxiv.org/abs/2106.14708v1
- Date: Mon, 28 Jun 2021 13:38:11 GMT
- Title: Weighted multi-level deep learning analysis and framework for processing
breast cancer WSIs
- Authors: Peter Bokor, Lukas Hudec, Ondrej Fabian, Wanda Benesova
- Abstract summary: We present a deep learning-based solution and framework for processing Whole Slide Images (WSI) based on a novel approach utilizing the advantages of image levels.
Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%.
- Score: 0.10499611180329801
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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%.
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