Abstract:
Metadata are key descriptors of research data, particularly for researchers
seeking to apply machine learning (ML) to the vast collections of digitized
specimens. Unfortunately, the available metadata is often sparse and, at
times, erroneous. Additionally, it is prohibitively expensive to address
these limitations through traditional, manual means. This paper reports on
research that applies machine-driven approaches to analyzing digitized fish
images and extracting various important features from them. The digitized
fish specimens are being analyzed as part of the Biology Guided Neural
Networks (BGNN) initiative, which is developing a novel class of artificial
neural networks using phylogenies and anatomy ontologies. Automatically
generated metadata is crucial for identifying the high-quality images needed
for the neural network’s predictive analytics. Methods that combine ML and
image informatics techniques allow us to rapidly enrich the existing metadata
associated with the 7,244 images from the Illinois Natural History Survey
(INHS) used in our study. Results show we can accurately generate many key
metadata properties relevant to the BGNN project, as well as general image
quality metrics (e.g. brightness and contrast). Results also show that we can
accurately generate bounding boxes and segmentation masks for fish, which are
needed for subsequent machine learning analyses. The automatic process
outperforms humans in terms of time and accuracy, and provides a novel
solution for leveraging digitized specimens in ML. This research demonstrates
the ability of computational methods to enhance the digital library services
associated with the tens of thousands of digitized specimens stored in
open-access repositories worldwide.
