Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays

Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays

Deep learning (DL) methods have demonstrated superior performance in medical image segmentation tasks. However, selecting a loss function that conforms to the data characteristics is critical for optimal performance. Further, the direct use of traditional DL models does not provide a measure of unce...

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Bibliographic Details
Main Authors: Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhiyun Xue, Stefan Jaeger, Sameer K. Antani
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Biomedicines
Subjects:
chest X-ray
uncertainty
uncertainty quantification
deep learning
medical image segmentation
tuberculosis
Online Access:https://www.mdpi.com/2227-9059/10/6/1323

Internet

https://www.mdpi.com/2227-9059/10/6/1323

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