用AI提升全息显微镜下花粉识别准确率，接近实际应用。
AI-Augmented Pollen Recognition in Optical and Holographic Microscopy for Veterinary Imaging
- 用GAN生成合成全息图像，缓解噪声和伪影问题。
- 检测准确率从8.15%提升至15.4%，分类准确率达54%。
- 适合做兽医影像自动化分析的研究者与开发者。
本文针对光学显微镜与数字散斑全息显微镜（DIHM）图像中的花粉自动识别开展全面研究。由于散斑噪声、孪生像伪影及与明场图像差异大，未重建的全息图像中花粉识别仍具挑战性。我们基于自动标注的双模态数据集，训练YOLOv8s进行目标检测，MobileNetV3L进行分类。在光学图像上，检测mAP50达91.3%，分类准确率97%；而在DIHM图像上，原始检测mAP50仅8.15%，分类准确率50%。通过扩展全息图像中花粉边界框，检测性能提升至13.3%，分类达54%。采用带谱归一化的Wasserstein GAN（WGAN-SN）生成合成DIHM图像，生成图像质量FID为58.246。将真实与合成数据以1.0:1.5比例混合后，检测性能进一步提升至15.4%。结果表明，基于GAN的数据增强可显著缩小性能差距，使全自动DIHM工作流在兽医影像中更接近实用。
原文摘要 · Abstract (English)
We present a comprehensive study on fully automated pollen recognition across both conventional optical and digital in-line holographic microscopy (DIHM) images of sample slides. Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substantial divergence from bright-field appearances. We establish the performance baseline by training YOLOv8s for object detection and MobileNetV3L for classification on a dual-modality dataset of automatically annotated optical and affinely aligned DIHM images. On optical data, detection mAP50 reaches 91.3% and classification accuracy reaches 97%, whereas on DIHM data, we achieve only 8.15% for detection mAP50 and 50% for classification accuracy. Expanding the bounding boxes of pollens in DIHM images over those acquired in aligned optical images achieves 13.3% for detection mAP50 and 54% for classification accuracy. To improve object detection in DIHM images, we employ a Wasserstein GAN with spectral normalization (WGAN-SN) to create synthetic DIHM images, yielding an FID score of 58.246. Mixing real-world and synthetic data at the 1.0 : 1.5 ratio for DIHM images improves object detection up to 15.4%. These results demonstrate that GAN-based augmentation can reduce the performance divide, bringing fully automated DIHM workflows for veterinary imaging a small but important step closer to practice.
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