Joshi, SmritiOsuala, RichardGarrucho, LidiaTsirikoglou, ApostoliaRiego, Javier delGwoździewicz, KatarzynaKushibar, KaisarDíaz, OliverLekadir, Karim, 1977-2025-03-252025-03-252024https://hdl.handle.net/2445/219964Medical image segmentation has improved with deep-learning methods, especially for tumor segmentation. However, variability in tumor shapes, sizes, and enhancement remains a challenge. Breast MRI adds further uncertainty due to anatomical differences. Informing clinicians about result reliability and using model uncertainty to improve predictions are essential. We study Monte-Carlo Dropout for generating multiple predictions and finding consensus segmentation. Our approach reduces false positives using per-pixel uncertainty and improves segmentation metrics. In addition, we study the correlation of model performance to the perceived ease of manual segmentation. Finally, we compare the per-pixel uncertainty with the inter-rater variability as segmented by six different radiologists. Our code is available at https://github.com/smriti-joshi/uncertainty-segmentation-mcdropout.git.9 p.application/pdfeng(c) SPIE, 2024Imatges mèdiquesAprenentatge automàticCàncer de mamaImaging systems in medicineMachine learningBreast cancerinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess