Unsupervised Representations of Pollen in Bright-Field Microscopy
This work addresses the challenge of analyzing microscopic biological structures like pollen with small or unlabeled datasets, though it is incremental as it applies existing unsupervised techniques to a new domain.
The authors tackled the problem of automated pollen analysis from bright-field microscopy images by developing an unsupervised deep learning method, achieving family-level identification using a dataset of 650 images.
We present the first unsupervised deep learning method for pollen analysis using bright-field microscopy. Using a modest dataset of 650 images of pollen grains collected from honey, we achieve family level identification of pollen. We embed images of pollen grains into a low-dimensional latent space and compare Euclidean and Riemannian metrics on these spaces for clustering. We propose this system for automated analysis of pollen and other microscopic biological structures which have only small or unlabelled datasets available.