发表机构
Cairo University（开罗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出双尺度组织病理图像与临床特征融合的机器学习框架，用于蕈样肉芽肿检测，图像模型准确率83.58%，临床模型达96.6%，支持早期筛查与分期。
AI 中文摘要
蕈样肉芽肿（MF）是一种罕见的皮肤T细胞淋巴瘤，由于其外观与良性炎症性皮肤病相似，早期阶段常被误诊。早期准确诊断对于改善患者预后至关重要。本文提出了一种全面的自动化MF检测诊断框架，该框架结合了双尺度组织病理图像分析与深度学习。为区分MF与其他淋巴增生性皮肤病，该方法利用双放大倍数（10倍和20倍）卷积神经网络（CNN）的晚期融合集成，并辅以基于16项临床特征训练的随机森林分类器。在包含463名患者、共6267张图像（其中4306张为MF，1961张为非MF）的扩展数据集上的实验结果表明，通过在更广泛的架构背景（10倍）中优先考虑更高分辨率的细胞学细节（20倍），可获得较强的检测性能。基于图像的晚期融合模型达到了83.58%的准确率和89.13%的灵敏度，而临床随机森林模型达到了96.6%的准确率和93.8%的灵敏度，突显了该多模态框架作为皮肤科稳健临床决策支持系统的潜力。该框架针对两个不同的临床目标：一个基于图像的双尺度流水线，用于MF与非MF皮肤病的早期诊断筛查；以及一个互补的临床元数据模型，用于后续对确诊MF病例（斑片/斑块与肿瘤）进行分期。
英文摘要
Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign inflammatory dermatoses. Early and accurate diagnosis is critical for improving patient outcomes. In this paper, we propose a comprehensive diagnostic framework for automated MF detection that combines dual- scale histopathological image analysis with deep learning. To distinguish MF from other lymphoproliferative skin conditions, the proposed approach leverages a late-fusion ensemble of dual- magnification (10x and 20x) convolutional neural networks (CNNs), complemented by a random forest classifier trained on 16 clinical features. Experimental results on an expanded dataset of 6,267 images (4,306 MF; 1,961 Non-MF) across 463 patients demonstrate that strong detection performance is obtained by prioritizing higher-resolution cytological details (20x) within broader architectural context (10x). The image-based late-fusion model achieves an accuracy of 83.58% and a sensitivity of 89.13%, while the clinical random forest model achieves an accuracy of 96.6% and sensitivity of 93.8%, highlighting the po- tential of this multimodal framework as a robust clinical decision support system in dermatology. This framework addresses two distinct clinical objectives: an image-based dual-scale pipeline optimized for the early diagnostic screening of MF versus non- MF dermatoses, and a complementary clinical metadata model designed for the subsequent staging of confirmed MF cases (patch/plaque versus tumor)
