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000265364 0247_ $$2doi$$a10.1109/ISBI53787.2023.10230497
000265364 037__ $$aDZNE-2023-00988
000265364 1001_ $$aKofler, Florian$$b0
000265364 1112_ $$a2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)$$cCartagena$$d2023-04-18 - 2023-04-21$$wColombia
000265364 245__ $$aApproaching Peak Ground Truth
000265364 260__ $$bIEEE$$c2023
000265364 29510 $$a2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) : [Proceedings] - IEEE, 2023. - ISBN 978-1-6654-7358-3 - doi:10.1109/ISBI53787.2023.10230497
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000265364 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1697198914_22039
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000265364 520__ $$aMachine 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.
000265364 536__ $$0G:(DE-HGF)POF4-354$$a354 - Disease Prevention and Healthy Aging (POF4-354)$$cPOF4-354$$fPOF IV$$x0
000265364 588__ $$aDataset connected to CrossRef Conference
000265364 7001_ $$0P:(DE-2719)9002062$$aWahle, Johannes$$b1$$udzne
000265364 7001_ $$aEzhov, Ivan$$b2
000265364 7001_ $$aWagner, Sophia J.$$b3
000265364 7001_ $$aAl-Maskari, Rami$$b4
000265364 7001_ $$aGryska, Emilia$$b5
000265364 7001_ $$aTodorov, Mihail$$b6
000265364 7001_ $$aBukas, Christina$$b7
000265364 7001_ $$aMeissen, Felix$$b8
000265364 7001_ $$aPeng, Tingying$$b9
000265364 7001_ $$aErtürk, Ali$$b10
000265364 7001_ $$aRueckert, Daniel$$b11
000265364 7001_ $$aHeckemann, Rolf$$b12
000265364 7001_ $$aKirschke, Jan$$b13
000265364 7001_ $$aZimmer, Claus$$b14
000265364 7001_ $$aWiestler, Benedikt$$b15
000265364 7001_ $$aMenze, Bjoern$$b16
000265364 7001_ $$aPiraud, Marie$$b17
000265364 773__ $$a10.1109/ISBI53787.2023.10230497
000265364 8564_ $$uhttps://pub.dzne.de/record/265364/files/DZNE-2023-00988_Restricted.pdf
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000265364 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)9002062$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b1$$kDZNE
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000265364 9141_ $$y2023
000265364 9201_ $$0I:(DE-2719)1013030$$kAG Mukherjee$$lStatistics and Machine Learning$$x0
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