用3D图像提升荨麻科花粉分类精度，助力过敏预警
Analysis of 3D Urticaceae Pollen Classification Using Deep Learning Models
- 基于完整3D图像堆栈，避免2D投影信息损失
- 最佳模型达98.3%的F1分数，显著优于传统方法
- 适合过敏研究与气象健康监测领域应用
由于气候变化，花粉症已成为日益严重的公共卫生问题，患者数量增多、持续时间延长且症状加重。精确的花粉分类有助于全年监测空气中的过敏原趋势，并指导城市预防策略。现有工作多依赖2D显微图像或从3D数据导出的2D投影。本文旨在使用完整的3D图像堆栈进行分类，并评估不同深度学习模型的表现。实验数据来自荨麻科（Urticaceae），特别是荨麻属（Urtica）和蝇子草属（Parietaria），二者形态相似但致敏性差异显著。通过优化层选择与训练轮数，预训练的ResNet3D模型取得最佳性能，F1-score达到98.3%。
原文摘要 · Abstract (English)
Due to the climate change, hay fever becomes a pressing healthcare problem with an increasing number of affected population, prolonged period of affect and severer symptoms. A precise pollen classification could help monitor the trend of allergic pollen in the air throughout the year and guide preventive strategies launched by municipalities. Most of the pollen classification works use 2D microscopy image or 2D projection derived from 3D image datasets. In this paper, we aim at using whole stack of 3D images for the classification and evaluating the classification performance with different deep learning models. The 3D image dataset used in this paper is from Urticaceae family, particularly the genera Urtica and Parietaria, which are morphologically similar yet differ significantly in allergenic potential. The pre-trained ResNet3D model, using optimal layer selection and extended epochs, achieved the best performance with an F1-score of 98.3%.
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