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Abstract and Figures

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.
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arXiv:1908.01866v1 [cs.CV] 5 Aug 2019
Unsupervised Representations of Pollen in Bright-Field Microscopy
Peter He * 1 Gerard Glowacki 2Alexis Gkantiragas * 3
Abstract
We present the first unsupervised deep learning
method for pollen analysis using bright-field mi-
croscopy. 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 Rie-
mannian metrics on these spaces for clustering.
We propose this system for automated analy-
sis of pollen and other microscopic biological
structures which have only small or unlabelled
datasets available.
1. Introduction
Pollen is formed as the male gametes of all flowering
plants. Palynology, the study of pollen (and spores), pro-
vides critical insights for a number of fields including foren-
sic science, ecology and agriculture (Mildenhall,2006;
Blackmore,2006;Zbrodsk & Vorlov,2014). For example,
pollen grains on the personal affects of an individual can
be used to ascertain whether they were at the scene of a
crime (Mildenhall,2006). However, traditional methods of
pollen analysis such as microscopic analysis of morphol-
ogy are time intensive and require trained specialists. As a
result, pollen analysis remains mostly inaccessible for the
majority of large scale applications. Making the analysis
of pollen fast, scalable and accessible can therefore open
the door to a great many opportunities hitherto unfeasible
in both commercial and academic domains.
While automated methods of pollen analysis have been de-
scribed in the literature as early as 2002 (Ronneberger et al.,
2002) the methods have not been implemented as signif-
icant tools in research or industry. Early attempts were
*Equal contribution 1Department of Computing, Imperial Col-
lege London 2Hampton Court House, London, United Kingdom
3Department of Molecular Biology, University College London.
Correspondence to: Peter He <peter.he18@imperial.ac.uk>,
Alexis Gkantiragas <alexis.gkantiragas.17@ucl.ac.uk>. With
thanks to Stewart McGown, University of St Andrews.
Workshop on Computational Biology at the International Confer-
ence on Machine Learning, Long Beach, CA, 2019. Copyright
2019 by the author(s).
largely scanning electron microscopy (Ronneberger et al.,
2002;Treloar et al.,2004) while later research used a
wide variety of microscopy types including dark field
(Lagerstrom et al.,2014;Pedersen et al.,2017), and fluo-
rescence microscopy (Mitsumoto et al.,2009). All of these
techniques require expensive equipment and most require
relatively skilled operators.
The introduction of deep learning techniques for pollen
analysis has shown much promise, however, have been
plagued by a lack of a significant labelled dataset. In a re-
cent publication, a supervised deep learning algorithm was
used to segment and then analyse pollen in honey samples
(He et al.,2018). However, the scope of this paper was lim-
ited by its requirement for the manual labelling of datasets.
Here we provide the first example of the use of an unsu-
pervised deep learning algorithm for the analysis of pollen
using bright-field microscopy.
2. Methods
Unlike previous almost-entirely supervised approaches to
pollen identification (which require large labelled datasets
of pollen to train), we propose a new unsupervised pipeline
(see Figure 1) for generating representations of pollen and
learning groupings of morphologically (and thererby often
taxonomically (Oswald et al.,2011)) similar pollen grains.
Figure 1.
Overview of pollen identification. (A) Image of slide.
(B) Downsampled image. (C) Object detection network. (D)
Bounding boxes. (E) Full resolution pollen grain crops. (F) En-
coder. (G) Decoder. (H) Further dimensionality reduction. (I)
Cluster assignment.
Unsupervised Representations of Pollen in Bright-Field Microscopy
Given an input image of a microscope slide containing
pollen, XR3328×3328×3
+, we first apply downsampling
such that XR416×416×3
+. The downsampled Xis then
passed through a YOLO-based (Redmon et al.,2016) ob-
ject detection network from (He et al.,2018) to obtain a set
of bounding boxes BR4
+×Nfor pollen grains present on
the slide containing indexed tuples (xi, yi, wi, hi)giving
the positions and dimensions of the ith particular bound-
ing box respectively. Each tuple in Bis then scaled up
component-wise by a factor of 8 and recombined with the
original image to obtain centered full-resolution crops ciof
the pollen. Each cipassed through an encoder based on the
VGG16 architecture from (Simonyan & Zisserman,2014)
pre-trained on ImageNet (Deng et al.,2009) with the final
fully-connected layers replaced with a 7×7max-pooling
layer resulting in a latent representation ziZR512 .
We then further reduce the dimensionality of zito df inal
Nand cluster the results using the k-means algorithm. In
our experiments, we compare principal component analysis
to the Isomap algorithm as dimensionality reduction meth-
ods as well as a Euclidean metric on Zto a Riemannian
metric induced by the latent space. The Riemannian met-
ric is approximated using a fixed-point algorithm described
in (Yang et al.,2018) and reflects the curvature that arises
from the non-linearity of the latent space which can be
characterised as a Riemannian manifold (Arvanitidis et al.,
2018).
In this way, we are able to generate representations of the
pollen without requiring a large dataset of pollen to learn
from. Furthermore, the clusters discovered can now be
used for a number of new tasks including semi-supervised
learning for pollen classification.
3. Experiments
Samples from a range of honeys (eucalpytus melliodora,
acacia and manuka) were spread onto a microscope slide
and immediately imaged. Images were taken once every
2 seconds on a Solomark compound microscope at 320x
zoom. This generated a dataset of 650 unlabelled bright-
field images of individual pollen.
3.1. Clustering
The system was run with parameters k= 10 and df inal =
3for ease of visualisation. The value of kwas selected
based on the ratio between decreases in cluster variance for
adjacent values of k. The system was in most cases able
to differentiate pollen morphology on (at the minimum) a
family level. For example, one cluster (see A in Figure 2)
shows a strong resemblance to pollen from the Myrtaceae
family (Sniderman et al.,2018).
There are marked differences in the latent spaces gener-
Figure 2.
Randomly sampled members from different clusters of
pollen imagery with PCA and a Euclidean metric on Z.
ated by the two different dimensionality reduction meth-
ods. Qualitatively, clusters are far more clear in the spaces
where Isomap was used (see Figure 4). Nonetheless, the
embeddings in the PCA spaces were sensible (see Figure
3). The clusters were not too different in terms of overall
concepts - the labels in Figure 5are matched to potential
”conceptual counterparts” in Figure 2. There were, how-
ever, qualitatively fewer obviously incorrect cluster assign-
ments from the Isomap-Riemannian space compared to the
other spaces.
We further visualise the curvature of the latent spaces by
sampling geodesics between random points on each man-
ifold. The geodesics observed on the PCA manifolds are
(unsurprisingly) effectively linear while some more inter-
esting curvature can be observed on the Isomap manifold
in Figure 6.
3.2. Comparison with Human-Defined Cluster
Assignments
A set of 30 bright-field images were collected at 320x zoom
using a combination of an AmScope bifocal microscope
and a Solomark compound microscope. Non-specialist vol-
Unsupervised Representations of Pollen in Bright-Field Microscopy
Figure 3.
Samples along a principal component of Zseemingly
encoding colour, surface texture and roundness.
Figure 4.
Latent space representations of Isomap (left) and PCA
(right). Cluster assignments are illustrated with lines from each
image point in the latent space to its respective centroid. Clusters
are computed using a Euclidean metric on Zin both visualisations.
Point size corresponds to depth.
unteers were instructed to categorise a random selection
of these images into the already generated clusters given
4 randomly chosen images from each cluster (similar to
Figure 2). The images were then reclassified by the sys-
tem (using PCA and an Euclidean metric on Z). Cases of
agreement between the human and system was 63%. Calcu-
lating Cohen’s Kappa coefficient, we find moderate agree-
ment κ= 0.576 (95% CI, 0.378 to 0.775).
4. Discussion
While previous research had used unsupervised methods
to analyse pollen samples in grass (Mander et al.,2013)
we used a sample size which was significantly larger and
did so with less expensive tools, making our method more
accessible. Additionally, we were able to utilize a signifi-
cantly larger dataset and observe an entire pollen grain, as
opposed to simply surface patterns. Moreover, agreement
between humans and our system was relatively high at 63%.
This implies that the clusters and assignments at large are
to some extent human-interperatable. An important future
benchmark would be comparison to human specialists who,
with the help of reference material, are able to visually dif-
ferentiate pollen at a species level with reasonable accuracy
though such resolution is not necessary for many applica-
tions (Mildenhall,2006).
While our system provides a new tool for working with
small or unlabelled datasets, it has significant limitations.
Figure 5.
Randomly sampled members from different clusters of
pollen imagery with Isomap and a Riemannian metric on Z.
Primarily, the small sample size and poor quality of some
images in our dataset limits the classification of pollen to
the family level. A larger dataset and better equipment
may be required to achieve genus and species level clas-
sification. Luckily, this should be relatively straightfor-
ward as no labels are needed. Alternatively, applying the
same system used here on datasets generated using other
microscopy types such as dark field or phase contrast mi-
croscopy could provide powerful new tools for rapid pollen
analysis, though this would potentially not confer the same
benefit with regards to accessibility.
Although the latent spaces observed in the experiments
were relatively sensible, often with the obvious clusters vis-
ible, it is likely that the embeddings are sub-optimal due to
the pre-training of the encoder on ImageNet. This is due to
the fact that biological imagery at the micro-scale is visu-
ally very different to much of the contents of ImageNet. It
would therefore perhaps be beneficial to representation to
use an encoder trained instead on a large dataset of micro-
scope imagery.
Unsupervised Representations of Pollen in Bright-Field Microscopy
Figure 6.
Visualising geodesics between random points on Zwith
Isomap and a Riemannian metric. The red lines are geodesics
while the blue dots are image embeddings from the dataset. They
are clearly non-linear over larger distances.
4.1. Applications
Beyond accelerating existing uses of pollen analysis, two
socially beneficial applications of large-scale automated
pollen analysis include combatting the proliferation of
fraudulent honey and as well as ecological monitoring.
Fraudulent honey is a global issue and has seen honey be-
come the third most faked food in worldwide (Moore et al.,
2012). Honey fraud is most commonly carried out through
mislabelling, dilution with cheaper honeys or sugar syrup
and a host of unethical beekeeping practices such as over-
harvesting. Existing authentication methods for honey have
been inaccurate (such as sugar or chemical testing) or
highly specialist and expensive (such as qPCR, LC-MS or
manual inspection of the pollen in honey (Mcdonald et al.,
2018;Kato et al.,2014;Sniderman et al.,2018)). For this
use case, we describe a system whereby producers of honey
may upload scans of their honey taken through low-cost
bright-field microscopy apparatus to an online database.
With such a database, consumers and retailers down the
supply chain will be able to verify the origin or authentic-
ity of their honey from its pollen profile without special-
ist knowledge. This will help combat the harmful effects
of fake honey on both the ecosystem and local economies
described elsewhere (F. Fairchild et al.,2000;Cairns et al.,
2005). Furthermore, the system will have made up the
cost of a microscope purchase within 3-4 jars of genuine
manuka honey.
In addition, we propose honey as a means for the large-
scale monitoring of flora biodiversity. Previous stud-
ies have used honey as a way of monitoring pollutants
(Smith et al.,2019) and conducting analysis on pollen
would allow for simultaneous generation of data on flora
biodiversity. This would go hand-in-hand with the honey
authentication system described above, using the pollen
profile database as a source of spatio-temporally tagged
samples. Two caveats, however, ought not be overlooked:
firstly, that this application would require a small quantity
of expertly labelled pollen data in order to associate pollen
grains with their respective botanical sources in a semi-
supervised fashion; and secondly, that honey bees have spe-
cific foraging habits which would need to be corrected for.
Nevertheless, pollen obtained from other sources such as
dedicated collection units can be equally analysed by the
system. We present a potential architecture for how both
proposed applications can co-exist in Figure 7.
Figure 7.
A potential architecture for ecosystem monitoring
through large-scale honey authentication infrastructure. The
database can not only be used for honey authentication, but also
for long-term analytics on changing pollen distributions.
Finally, the system could be used for the analysis of any un-
labelled microscopy dataset in the environmental and life
sciences. Soil fungi, for example, are considered to be an
excellent indicator of soil fertility (Kranabetter et al.,2009).
The use of the system for such applications could acceler-
ate the development of prototypes and proofs-of-concept
by removing the need for a large labelled dataset.
5. Conclusions
Despite its limitations, the system we have described forms
the groundwork for a powerful, scalable and accessible tool
for pollen analysis. The acquisition of a larger dataset and
an application-specific encoder are needed to bring the sys-
tem to a level of maturity which, once reached, will open
doors to novel techniques for ecological monitoring, honey
authentication and a variety of other use cases.
6. Acknowledgements
Many thanks to Stewart McGown at the University of St
Andrews for assisting in the design and prototyping of the
large-scale honey authentication system. We also acknowl-
edge Raghvi Arya for her advice regarding the poster pre-
sentation at ICML.
Unsupervised Representations of Pollen in Bright-Field Microscopy
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