Proper Scoring Loss Functions Are Simple and Effective for Uncertainty Quantification of White Matter Hyperintensities. - Ben Philps; Maria del C. Valdés Hernández; Miguel Bernabeu Llinares (UNSURE@MICCAI, 2023)

Stochastic Uncertainty Quantification Techniques Fail to Account for Inter-analyst Variability in White Matter Hyperintensity Segmentation. - Ben Philps; Maria del C. Valdés Hernández; Susana Muñoz Maniega; Mark E. Bastin; Eleni Sakka; Una Clancy; Joanna M. Wardlaw; Miguel O. Bernabeu (MIUA (1), 2024)

InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation. - Thibaut Goldsborough; Ben Philps; Alan O'Callaghan; Fiona Inglis; Leo Leplat; Andrew Filby; Hakan Bilen; Peter Bankhead (CoRR, 2024)

Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification. - Ben Philps; Maria del C. Valdés Hernández; Chen Qin; Una Clancy; Eleni Sakka; Susana Muñoz Maniega; Mark E. Bastin; Angela C. C. Jochems; Joanna M. Wardlaw; Miguel O. Bernabeu; Alzheimer's Disease Neuroimaging Initiative (CoRR, 2024)

Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification. - Ben Philps; Maria del C. Valdés Hernández; Chen Qin; Una Clancy; Eleni Sakka; Susana Muñoz Maniega; Mark E. Bastin; Angela C. C. Jochems; Joanna M. Wardlaw; Miguel O. Bernabeu (Medical Image Anal., 2025)