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Natural vs Balanced Distribution in Deep Learning on Whole Slide Images for Cancer Detection

Abstract : The class distribution of data is one of the factors that regulates the performance of machine learning models. However, investigations on the impact of different distributions available in the literature are very few, sometimes absent for domain-specific tasks. In this paper, we analyze the impact of natural and balanced distributions of the training set in deep learning (DL) models applied on histological images, also known as whole slide images (WSIs). WSIs are considered as the gold standard for cancer diagnosis. In recent years, researchers have turned their attention to DL models to automate and accelerate the diagnosis process. In the training of such DL models, filtering out the non-regions-of-interest from the WSIs and adopting an artificial distribution-usually a balanced distribution-is a common trend. In our analysis, we show that keeping the WSIs data in their usual distribution-which we call natural distribution-for DL training is better than the artificially obtained balanced distribution. We conduct an empirical comparative study with 10 random folds for each distribution, comparing the resulting average performance levels in terms of five different evaluation metrics. Experimental results show the effectiveness of the natural distribution over the balanced one across all the evaluation metrics. CCS CONCEPTS • Computing methodologies → Supervised learning; Image processing; Image segmentation; • Applied computing → Health informatics.
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https://hal.archives-ouvertes.fr/hal-03057209
Contributor : Ismat Ara Reshma <>
Submitted on : Friday, December 11, 2020 - 3:55:41 PM
Last modification on : Saturday, June 12, 2021 - 8:30:03 PM
Long-term archiving on: : Friday, March 12, 2021 - 8:00:10 PM

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  • HAL Id : hal-03057209, version 1
  • ARXIV : 2012.11684

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Ismat Ara Reshma, Sylvain Cussat-Blanc, Radu Tudor Ionescu, Hervé Luga, Josiane Mothe. Natural vs Balanced Distribution in Deep Learning on Whole Slide Images for Cancer Detection. 36th ACM/SIGAPP Symposium on Applied Computing (SAC 2021), Association for Computing Machinery (ACM) - Special Interest Group on Applied Computing (SIGAPP), Mar 2021, Virtual Event, South Korea. ⟨hal-03057209⟩

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