4 papers
Explaining Uncertainty in Multiple Sclerosis Cortical Lesion Segmentation Beyond Prediction Errors
Nataliia Molchanova, Pedro M. Gordaliza, Alessandro Cagol +14
Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Explainable AI and uncertainty quantificati…
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Federico Spagnolo, Nataliia Molchanova, Meritxell Bach Cuadra +5
Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-…
Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
Enamundram Naga Karthik, Sandrine Bédard, Jan Valošek +53
Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. Whil…
Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis
Nataliia Molchanova, Alessandro Cagol, Mario Ocampo-Pineda +15
Cortical lesions (CLs) have emerged as valuable biomarkers in multiple sclerosis (MS), offering high diagnostic specificity and prognostic relevance. However, their routine clinica…