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Kofler, F. ; Wahle, J.* ; Ezhov, I.* ; Wagner, S. ; Al-Maskari, R. ; Gryska, E.* ; Todorov, M.I. ; Bukas, C. ; Meissen, F.* ; Peng, T. ; Ertürk, A. ; Rueckert, D.* ; Heckemann, R.* ; Kirschke, J.* ; Zimmer, C.* ; Wiestler, B.* ; Menze, B.* ; Piraud, M.

Approaching Peak Ground Truth.

In: (Proceedings - International Symposium on Biomedical Imaging, 18-21 April 2023, Cartagena, Colombia). 345 E 47th St, New York, Ny 10017 Usa: Ieee, 2023. 6 (Proceedings - International Symposium on Biomedical Imaging ; 2023-April)
DOI
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical domain, annotations are subjective and suffer from low inter-and intra-rater reliability. Since annotations only reflect one interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, four categories of PGT-aware strategies to evaluate and improve model performance are reviewed.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Annotation ; Deep Learning ; Ground Truth ; Machine Learning ; Reference ; Segmentation; Label Noise; Classification
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Konferenztitel Proceedings - International Symposium on Biomedical Imaging
Konferzenzdatum 18-21 April 2023
Konferenzort Cartagena, Colombia
Quellenangaben Band: 2023-April, Heft: , Seiten: 6 Artikelnummer: , Supplement: ,
Verlag Ieee
Verlagsort 345 E 47th St, New York, Ny 10017 Usa
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530001-001
G-530006-001
G-505800-001
Förderungen
Helmut Horten Foundation
BMBF
DFG
ERC
Technical University of Munich - Institute for Advanced Study - German Excellence Initiative
Add-on Fellowship of the Joachim Herz Foundation
Helmholtz Association under the joint research school "Munich School for Data Science MUDS"
DComEX
Translational Brain Imaging Training Network (TRABIT) under the European Union's 'Horizon 2020' research & innovation program
Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE)
DOI 10.1109/ISBI53787.2023.10230497
WOS ID 001062050500175
Scopus ID 85172164293
Erfassungsdatum 2023-10-18
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