Using Pure Pollen Species When Training a CNN to Segment Pollen Mixtures

Abstract

Recognizing the types of pollen grains and estimating their proportion in pollen mixture samples collected in a specific geographical area is important for agricultural, medical, and ecosystem research. Our paper adopts a convolutional neural network for the automatic segmentation of pollen species in microscopy images, and proposes an original strategy to train such network at reasonable manual annotation cost. Our approach is founded on a large dataset composed of pure pollen images. It first (semi-)manually segments foreground, i.e. pollen grains, and background in a fraction of those images, and use the resulting annotated dataset to train a universal pollen segmentation CNN. In the second step, this model is used to automatically segment a large number of additional pure pollen images, so as to supervise the training of a pollen species segmentation model. Despite the fact that it has been trained from pure images only, the model is shown to provide accurate segmentation of species in pollen mixtures. Our experiments also demonstrate that dedicating a model to the segmentation of a subset of the available pure pollen species makes it possible to train a bin pollen class, corresponding to pollen species that are not in the subset of species recognized by the model. This strategy is useful to cope with unexpected species in a mixture.

On the Importance of Diversity When Training Deep Learning Segmentation Models with Error-Prone Pseudo-Labels

“…The CNN network used in our work consists of an encoder and decoder, following a U-Net shape [49,50]. Compared to the U-net introduced in [51], we use a seven-layer network. Each layer in the network consists of a convolution operation, followed by batch normalization and the Rectified Linear Unit (ReLU) activation function.…”