AI-Augmented Pollen Recognition in Optical and Holographic Microscopy for Veterinary Imaging

TL;DR

This study explores automated pollen recognition using optical and holographic microscopy, employing deep learning and GAN-based data augmentation to improve detection and classification accuracy, especially in challenging holographic images.

Contribution

The paper introduces a novel approach combining deep learning with GAN-generated synthetic images to enhance pollen detection in holographic microscopy.

Findings

Optical microscopy achieves 91.3% detection mAP50 and 97% classification accuracy.

Holographic microscopy detection is initially low at 8.15%, improved to 15.4% with synthetic data.

GAN-based augmentation reduces the performance gap between optical and holographic pollen recognition.

Abstract

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.