文章：

病理学新视角：基于增强视觉Transformer的结直肠癌早期检测

Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer

原文发布日期：8 April 2024

DOI: 10.3390/cancers16071441

类型: Article

开放获取: 是

英文摘要：

Endoscopic pathological findings of the gastrointestinal tract are crucial for the early diagnosis of colorectal cancer (CRC). Previous deep learning works, aimed at improving CRC detection performance and reducing subjective analysis errors, are limited to polyp segmentation. Pathological findings were not considered and only convolutional neural networks (CNNs), which are not able to handle global image feature information, were utilized. This work introduces a novel vision transformer (ViT)-based approach for early CRC detection. The core components of the proposed approach are ViTCol, a boosted vision transformer for classifying endoscopic pathological findings, and PUTS, a vision transformer-based model for polyp segmentation. Results demonstrate the superiority of this vision transformer-based CRC detection method over existing CNN and vision transformer models. ViTCol exhibited an outstanding performance in classifying pathological findings, with an area under the receiver operating curve (AUC) value of 0.9999 ± 0.001 on the Kvasir dataset. PUTS provided outstanding results in segmenting polyp images, with mean intersection over union (mIoU) of 0.8673 and 0.9092 on the Kvasir-SEG and CVC-Clinic datasets, respectively. This work underscores the value of spatial transformers in localizing input images, which can seamlessly integrate into the main vision transformer network, enhancing the automated identification of critical image features for early CRC detection.

摘要翻译： 

胃肠道内镜病理表现对结直肠癌的早期诊断至关重要。既往旨在提升结直肠癌检测性能、减少主观分析误差的深度学习研究多局限于息肉分割任务，既未纳入病理表现分析，又仅采用无法处理全局图像特征信息的卷积神经网络。本研究提出一种基于视觉Transformer的结直肠癌早期检测新方法。该方法的核心理念在于构建ViTCol——用于内镜病理表现分类的增强型视觉Transformer，以及PUTS——基于视觉Transformer的息肉分割模型。实验结果表明，这种基于视觉Transformer的结直肠癌检测方法在性能上显著优于现有卷积神经网络及视觉Transformer模型。ViTCol在病理表现分类任务中表现卓越，在Kvasir数据集上获得0.9999±0.001的受试者工作特征曲线下面积值；PUTS在息肉图像分割任务中取得优异成果，在Kvasir-SEG和CVC-Clinic数据集上的平均交并比分别达到0.8673和0.9092。本研究凸显了空间Transformer在定位输入图像方面的价值，其可无缝集成至主视觉Transformer网络，从而增强对结直肠癌早期诊断关键图像特征的自动化识别能力。

原文链接：

Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer