Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation

no code implementations • 1 Feb 2024 • Imane Hamzaoui, Hadjer Benmeziane, Zayneb Cherif, Kaoutar El Maghraoui

This work investigates the role of the emerging Analog In-memory computing (AIMC) paradigm in enabling Medical AI analysis and improving the certainty of these models at the edge.

Medical Image Segmentation Uncertainty Quantification