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Nyaz Jalal on Muck Rack

Nyaz Jalal

As seen in: Nature, MDPI
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Articles

Analysis of annotation requirements for training uncertainty-aware image segmentation models

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By Nyaz Jalal, Zbisław Tabor
| Nature Verified
Abstract Reliable uncertainty quantification is essential for deploying image segmentation models in systems where inter-rater variability among experts is significant and must be accounted for to ensure dependable performance. A key unresolved question is whether multiple annotations per case are required during training to obtain robust uncertainty estimates. In this work, we provide analytical and empirical evidence addressing this issue.
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Evaluating Uncertainty Quantification in Medical Image Segmentation: A Multi-Dataset, Multi-Algorithm Study

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By Małgorzata A. Śliwińska, Wadim Wojciechowski, Iwona Kucybała, Nyaz Jalal
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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