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...

Descripción completa

Detalles Bibliográficos
Autores principales: Rajaraman, Sivaramakrishnan, Zamzmi, Ghada, Yang, Feng, Xue, Zhiyun, Jaeger, Stefan, Antani, Sameer K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220007/
https://www.ncbi.nlm.nih.gov/pubmed/35740345
http://dx.doi.org/10.3390/biomedicines10061323

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220007/
https://www.ncbi.nlm.nih.gov/pubmed/35740345
http://dx.doi.org/10.3390/biomedicines10061323

Ejemplares similares