PuSH - Publikationsserver des Helmholtz Zentrums München

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)
Verlagsversion 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
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Deep Learning ; Pollen Recognition ; Sample Difficulty Analysis
ISSN (print) / ISBN 2405-8440
e-ISSN 2405-8440
Zeitschrift Heliyon
Quellenangaben Band: 11, Heft: 2, Seiten: , Artikelnummer: e41656 Supplement: ,
Verlag Elsevier
Verlagsort London [u.a.]
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Environmental Medicine (IEM)