胰:基于云的胰腺癌自动分级系统

IF 1.3 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Muhammad Nurmahir Mohamad Sehmi, M. F. A. Fauzi, Wan Siti Halimatul Munirah Wan Ahmad, E. Chan
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引用次数: 0

摘要

胰腺癌是最致命的疾病之一,在过去的20年里夺走了数百万人的生命。由于胰腺癌分级面临的挑战,本研究提出了一种基于云的自动化系统,利用卷积神经网络深度学习(DL)方法将病理图像中的四类胰腺癌分级分为正常、I级、II级和III级。这个基于云的系统被命名为胰腺系统,它从网络用户界面输入高倍场图像,将它们切成小块,做出预测,在将最终结果返回给病理学家之前将这些小块缝合起来。Anvil和Google Colab作为系统的骨干,构建一个web用户界面,用于部署深度学习模型在癌症等级分类中。本研究采用迁移学习方法对预训练的DenseNet201模型进行数据增强,以减轻小数据集的挑战。在将数据集分成80%的训练集和20%的验证集时,采用5倍交叉验证(CV)来确保使用数据集中的所有样本来评估和减轻选择偏差。实验在三个不同的数据集(May Grunwald-Giemsa (MGG),苏木精和伊红(H&E),以及两者的混合物,称为混合数据集)上进行,以观察模型在两种不同病理染色(MGG和H&E)上的性能。据报道,在从病理图像预测胰腺癌分级方面表现良好,MGG、H&E和Mixed数据集的平均f1评分分别为0.88、0.96和0.89。本研究的结果有望作为病理学家的预后系统,为胰腺癌的病理图像提供准确的分级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System
Pancreatic cancer is one of the deadliest diseases which has taken millions of lives over the past 20 years. Due to challenges in grading pancreatic cancer, this study presents an automated cloud-based system, utilizing a convolutional neural network deep learning (DL) approach to classifying four classes of pancreatic cancer grade from pathology image into Normal, Grade I, Grade II, and Grade III. This cloud-based system, named PancreaSys, takes an input of high power field images from the web user interface, slices them into smaller patches, makes predictions, and stitches back the patches before returning the final result to the pathologist. Anvil and Google Colab are used as the backbone of the system to build a web user interface for deploying the DL model in the classification of the cancer grade. This work employs the transfer learning approach on a pre-trained DenseNet201 model with data augmentation to alleviate the small dataset’s challenges. A 5-fold cross-validation (CV) was employed to ensure all samples in a dataset were used to evaluate and mitigate selection bias during splitting the dataset into 80% training and 20% validation sets. The experiments were done on three different datasets (May Grunwald-Giemsa (MGG), hematoxylin and eosin (H&E), and a mixture of both, called the Mixed dataset) to observe the model performance on two different pathology stains (MGG and H&E). Promising performances are reported in predicting the pancreatic cancer grade from pathology images, with a mean f1-score of 0.88, 0.96, and 0.89 for the MGG, H&E, and Mixed datasets, respectively. The outcome from this research is expected to serve as a prognosis system for the pathologist in providing accurate grading for pancreatic cancer in pathological images.
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