Abstract
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. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. 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. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.
Competing Interest Statement
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.
Footnotes
Updated Declaration of Interest Statement.
Abbreviations
- AI
- Artificial Intelligence
- DIA
- Digital Image Analysis
- GUI
- Graphical User Interface
- ROC
- Receiver-Operator Characteristic
- AUC
- Area Under Curve