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| 001 | 265364 | ||
| 005 | 20231014131029.0 | ||
| 024 | 7 | _ | |a 10.1109/ISBI53787.2023.10230497 |2 doi |
| 037 | _ | _ | |a DZNE-2023-00988 |
| 100 | 1 | _ | |a Kofler, Florian |b 0 |
| 111 | 2 | _ | |a 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) |c Cartagena |d 2023-04-18 - 2023-04-21 |w Colombia |
| 245 | _ | _ | |a Approaching Peak Ground Truth |
| 260 | _ | _ | |c 2023 |b IEEE |
| 295 | 1 | 0 | |a 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) : [Proceedings] - IEEE, 2023. - ISBN 978-1-6654-7358-3 - doi:10.1109/ISBI53787.2023.10230497 |
| 300 | _ | _ | |a 1-6 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1697198914_22039 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 520 | _ | _ | |a 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. © 2023 IEEE. |
| 536 | _ | _ | |a 354 - Disease Prevention and Healthy Aging (POF4-354) |0 G:(DE-HGF)POF4-354 |c POF4-354 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Wahle, Johannes |0 P:(DE-2719)9002062 |b 1 |u dzne |
| 700 | 1 | _ | |a Ezhov, Ivan |b 2 |
| 700 | 1 | _ | |a Wagner, Sophia J. |b 3 |
| 700 | 1 | _ | |a Al-Maskari, Rami |b 4 |
| 700 | 1 | _ | |a Gryska, Emilia |b 5 |
| 700 | 1 | _ | |a Todorov, Mihail |b 6 |
| 700 | 1 | _ | |a Bukas, Christina |b 7 |
| 700 | 1 | _ | |a Meissen, Felix |b 8 |
| 700 | 1 | _ | |a Peng, Tingying |b 9 |
| 700 | 1 | _ | |a Ertürk, Ali |b 10 |
| 700 | 1 | _ | |a Rueckert, Daniel |b 11 |
| 700 | 1 | _ | |a Heckemann, Rolf |b 12 |
| 700 | 1 | _ | |a Kirschke, Jan |b 13 |
| 700 | 1 | _ | |a Zimmer, Claus |b 14 |
| 700 | 1 | _ | |a Wiestler, Benedikt |b 15 |
| 700 | 1 | _ | |a Menze, Bjoern |b 16 |
| 700 | 1 | _ | |a Piraud, Marie |b 17 |
| 773 | _ | _ | |a 10.1109/ISBI53787.2023.10230497 |
| 856 | 4 | _ | |u https://pub.dzne.de/record/265364/files/DZNE-2023-00988_Restricted.pdf |
| 856 | 4 | _ | |u https://pub.dzne.de/record/265364/files/DZNE-2023-00988_Restricted.pdf?subformat=pdfa |x pdfa |
| 909 | C | O | |o oai:pub.dzne.de:265364 |p VDB |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 1 |6 P:(DE-2719)9002062 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Neurodegenerative Diseases |1 G:(DE-HGF)POF4-350 |0 G:(DE-HGF)POF4-354 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Disease Prevention and Healthy Aging |x 0 |
| 914 | 1 | _ | |y 2023 |
| 920 | 1 | _ | |0 I:(DE-2719)1013030 |k AG Mukherjee |l Statistics and Machine Learning |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a contb |
| 980 | _ | _ | |a I:(DE-2719)1013030 |
| 980 | _ | _ | |a UNRESTRICTED |
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