Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images

PURPOSE: Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels (“image contains object” or “imag...

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Detalles Bibliográficos
Autores principales: O’Shea, Robert, Manickavasagar, Thubeena, Horst, Carolyn, Hughes, Daniel, Cusack, James, Tsoka, Sophia, Cook, Gary, Goh, Vicky
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657919/
https://www.ncbi.nlm.nih.gov/pubmed/37980637
http://dx.doi.org/10.1186/s13244-023-01542-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657919/
https://www.ncbi.nlm.nih.gov/pubmed/37980637
http://dx.doi.org/10.1186/s13244-023-01542-2

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