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Milling, M.* ; Rampp, S.D.N.* ; Triantafyllopoulos, A.* ; Plaza, M.P. ; Brunner, J.O.* ; Traidl-Hoffmann, C. ; Schuller, B.W.* ; Damialis, A.*

Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers.

Heliyon 11:e41656 (2025)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Deep-learning-based classification of pollen grains has been a major driver towards automatic monitoring of airborne pollen. Yet, despite an abundance of available datasets, little effort has been spent to investigate which aspects pose the biggest challenges to the (often black-box- resembling) pollen classification approaches. To shed some light on this issue, we conducted a sample-level difficulty analysis based on the likelihood for one of the largest automatically-generated datasets of pollen grains on microscopy images and investigated the reason for which certain airborne samples and specific pollen taxa pose particular problems to deep learning algorithms. It is here concluded that the main challenges lie in A) the (partly) co-occurring of multiple pollen grains in a single image, B) the occlusion of specific markers through the 2D capturing of microscopy images, and C) for some taxa, a general lack of salient, unique features. Our code is publicly available under https://github.com/millinma/SDPollen
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Deep Learning ; Pollen Recognition ; Sample Difficulty Analysis
ISSN (print) / ISBN 2405-8440
e-ISSN 2405-8440
Journal Heliyon
Quellenangaben Volume: 11, Issue: 2, Pages: , Article Number: e41656 Supplement: ,
Publisher Elsevier
Publishing Place London [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Environmental Medicine (IEM)