HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks.

Autor:	McCombe KD; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland., Craig SG; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland., Viratham Pulsawatdi A; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland., Quezada-Marín JI; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland., Hagan M; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland., Rajendran S; Belfast Health and Social Care Trust, Belfast, Northern Ireland., Humphries MP; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland., Bingham V; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland., Salto-Tellez M; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland.; Belfast Health and Social Care Trust, Belfast, Northern Ireland.; The Institute of Cancer Research, London United Kingdom., Gault R; The School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, Northern Ireland., James JA; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland.; Belfast Health and Social Care Trust, Belfast, Northern Ireland.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2021 Aug 26; Vol. 19, pp. 4840-4853. Date of Electronic Publication: 2021 Aug 26 (Print Publication: 2021).
DOI:	10.1016/j.csbj.2021.08.033
Abstrakt:	The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. This generally requires consultation of documentation for multiple coding libraries, as well as trial and error to ensure that the techniques used on the images are appropriate. Herein we introduce HistoClean; a user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. HistoClean is an application that aims to help bridge the knowledge gap between pathologists, biomedical scientists and computer scientists by providing transparent image augmentation and pre-processing techniques which can be applied without prior coding knowledge. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. This study demonstrates how HistoClean can be used to improve a standard deep learning workflow via classical image augmentation and pre-processing techniques, even with a relatively simple convolutional neural network architecture. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean. Competing Interests: Dr. M.S.T has recently received honoraria for advisory work in relation to the following companies: Incyte, MindPeak, QuanPathDerivatives and MSD. He is part of academia-industry consortia supported by the UK government (Innovate UK). Dr J.J. is also involved in an academic-industry research programme funded by IUK. These declarations of interest are all unrelated with the submitted publication. All other authors declare no competing interests. (© 2021 The Authors.)
Databáze:	MEDLINE
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