Department of Computer Architecture and Technology, University of Granada, E.T.S. de Ingenierías Informática y de Telecomunicación, C/ Periodista Daniel Saucedo Aranda S/N CP:18071 Granada, Spain.
Department of Computer Architecture and Technology, University of Granada, E.T.S. de Ingenierías Informática y de Telecomunicación, C/ Periodista Daniel Saucedo Aranda S/N CP:18071 Granada, Spain.
Comput Biol Med. 2024 Jan;168:107713. doi: 10.1016/j.compbiomed.2023.107713. Epub 2023 Nov 19.
Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%. The code is available at https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images.
癌症是世界上最重要的病理学之一，因为它导致数百万人死亡，而且在大多数情况下，这种疾病的治疗方法是有限的。快速传播是这种疾病的最重要特征之一，因此许多努力都集中在早期检测和定位上。在人工智能（AI）的帮助下，医学在最近几十年取得了许多进展，降低了成本并节省了时间。在本文中，使用深度学习模型（DL）提出了一种新的方法，用于检测和定位 WSI 图像中的癌变区域，使用组织斑块叠加来提高性能结果。提出并讨论了一种新的重叠方法，以及不同的替代方法来评估同一区域中重叠斑块的标签，以提高检测性能。目标是通过对多个重叠斑块测试的不同区域进行多次标记来增强图像的不同区域的标记。结果表明，所提出的方法改进了传统框架，并提供了一种新的癌症检测方法。所提出的方法基于在重叠斑块标签上应用 3x3 步 2 平均池化滤波器，在 HUP 数据集上提供了更好的结果，对于误分类的斑块有 12.9%的校正率，在 CINIJ 数据集上有 15.8%的校正率。此外，实现了一个滤波器来纠正也被误分类的孤立斑块。最后，对 CNN 决策阈值进行了研究，以分析阈值对模型准确性的影响。随着阈值决策的改变，加上用于孤立斑块的滤波器和用于重叠斑块的建议方法，可以纠正传统方法中约 20%的误标记斑块。总的来说，所提出的方法的准确率达到了 94.6%。代码可在 https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images 获得。
