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.

过去十年数字病理学的发展为癌症预测和预后开辟了新的研究途径和见解。特别是，用于分析数字图像的深度学习和计算机视觉技术激增。该领域的常见做法是使用图像预处理和增强来防止偏差和过度拟合，从而创建更强大的深度学习模型。这通常需要查阅多个编码库的文档，以及反复试验以确保用于图像的技术是合适的。这里我们介绍 HistoClean；一个用户友好的图形用户界面，将多个图像处理模块整合到一个易于使用的工具包中。

HistoClean 是一个应用程序，旨在通过提供透明的图像增强和预处理技术来帮助弥合病理学家、生物医学科学家和计算机科学家之间的知识差距，这些技术无需先验编码知识即可应用。

在这项研究中，我们利用 HistoClean 对用于检测基质成熟度的简单卷积神经网络的图像进行预处理，从而提高模型在切片、感兴趣区域和患者级别的准确性。本研究展示了 HistoClean 如何通过经典图像增强和预处理技术来改进标准深度学习工作流程，即使使用相对简单的卷积神经网络架构也是如此。HistoClean 是免费的开源软件，可以从这里的 Github 存储库下载：https://github.com/HistoCleanQUB/HistoClean。