Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China.
Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China.
Phys Med. 2023 Mar;107:102534. doi: 10.1016/j.ejmp.2023.102534. Epub 2023 Feb 17.
Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Therefore, a multi-category colorectal cancer dataset is needed to test various medical image classification methods to find high classification accuracy and strong robustness.
A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset (EBHI) is published in this paper. To demonstrate the effectiveness of the EBHI dataset, we have utilized several machine learning, convolutional neural networks and novel transformer-based classifiers for experimentation and evaluation, using an image with a magnification of 200×.
Experimental results show that the deep learning method performs well on the EBHI dataset. Classical machine learning methods achieve maximum accuracy of 76.02% and deep learning method achieves a maximum accuracy of 95.37%.
To the best of our knowledge, EBHI is the first publicly available colorectal histopathology enteroscope biopsy dataset with four magnifications and five types of images of tumor differentiation stages, totaling 5532 images. We believe that EBHI could attract researchers to explore new classification algorithms for the automated diagnosis of colorectal cancer, which could help physicians and patients in clinical settings.
结直肠癌已成为全球第三大常见癌症，约占癌症患者的 10%。早期发现该疾病对于结直肠癌患者的治疗至关重要。组织病理学检查是筛查结直肠癌的金标准。然而，目前缺乏结直肠癌的组织病理学图像数据集，特别是结肠镜活检，这阻碍了计算机辅助诊断技术的准确评估。因此，需要一个多类别结直肠癌数据集来测试各种医学图像分类方法，以找到高分类精度和强稳健性。
本文发布了一个新的公开可用的结肠镜活检组织病理学 H&E 图像数据集（EBHI）。为了展示 EBHI 数据集的有效性，我们利用几种机器学习、卷积神经网络和新型基于转换器的分类器进行了实验和评估，使用放大倍数为 200×的图像。
实验结果表明，深度学习方法在 EBHI 数据集上表现良好。经典机器学习方法的最大准确率为 76.02%，而深度学习方法的最大准确率为 95.37%。
据我们所知，EBHI 是第一个公开的结直肠组织病理学结肠镜活检数据集，具有四个放大倍数和五种肿瘤分化阶段的图像类型，总共有 5532 张图像。我们相信，EBHI 可以吸引研究人员探索用于结直肠癌自动诊断的新分类算法，这有助于临床环境中的医生和患者。
