A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network

A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network

Abstract This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled...

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Bibliographic Details
Main Authors: Murtaza Ashraf, Willmer Rafell Quiñones Robles, Mujin Kim, Young Sin Ko, Mun Yong Yi
Format: Article
Language:English
Published: Nature Portfolio 2022-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-05001-8

Internet

https://doi.org/10.1038/s41598-022-05001-8

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