标题： 先进的混合深度学习模型用于骨肉瘤组织病理学图像的增强分类 显示英文标题

标题： Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images

摘要： 机器学习的最新进展正在改变医学图像分析，特别是在癌症检测和分类方面。 深度学习技术，尤其是卷积神经网络（CNN）和视觉Transformer（ViT），现在能够精确分析复杂的组织病理学图像，实现检测自动化并提高各种癌症类型的分类准确性。 本研究专注于骨肉瘤（OS），这是儿童和青少年中最常见的骨癌，影响手臂和腿部的长骨。 早期且准确的OS检测对于改善患者预后和降低死亡率至关重要。 然而，癌症发病率的增加以及个性化治疗的需求，在实现精准诊断和定制疗法方面带来了挑战。 我们提出了一种新的混合模型，结合了卷积神经网络（CNN）和视觉Transformer（ViT），以提高使用苏木精和伊红（H&E）染色的组织病理学图像的OS诊断准确性。 CNN模型提取局部特征，而ViT捕捉组织病理学图像的全局模式。 这些特征通过多层感知机（MLP）组合并分类为四个类别：非肿瘤（NT）、非存活肿瘤（NVT）、存活肿瘤（VT）和无存活比（NVR）。 使用癌症影像档案（TCIA）数据集，该模型实现了99.08%的准确率、99.10%的精确度、99.28%的召回率和99.23%的F1分数。 这是首次使用此数据集成功完成四类分类，为OS研究设定了新的基准，并为未来的诊断进步提供了有希望的潜力。 显示英文摘要

摘要： Recent advances in machine learning are transforming medical image analysis, particularly in cancer detection and classification. Techniques such as deep learning, especially convolutional neural networks (CNNs) and vision transformers (ViTs), are now enabling the precise analysis of complex histopathological images, automating detection, and enhancing classification accuracy across various cancer types. This study focuses on osteosarcoma (OS), the most common bone cancer in children and adolescents, which affects the long bones of the arms and legs. Early and accurate detection of OS is essential for improving patient outcomes and reducing mortality. However, the increasing prevalence of cancer and the demand for personalized treatments create challenges in achieving precise diagnoses and customized therapies. We propose a novel hybrid model that combines convolutional neural networks (CNN) and vision transformers (ViT) to improve diagnostic accuracy for OS using hematoxylin and eosin (H&E) stained histopathological images. The CNN model extracts local features, while the ViT captures global patterns from histopathological images. These features are combined and classified using a Multi-Layer Perceptron (MLP) into four categories: non-tumor (NT), non-viable tumor (NVT), viable tumor (VT), and none-viable ratio (NVR). Using the Cancer Imaging Archive (TCIA) dataset, the model achieved an accuracy of 99.08%, precision of 99.10%, recall of 99.28%, and an F1-score of 99.23%. This is the first successful four-class classification using this dataset, setting a new benchmark in OS research and offering promising potential for future diagnostic advancements.