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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...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Vienna
2023
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| Materias: | |
| 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 |