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

arXiv:2403.08796v1 Announce Type: cross
Abstract: 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. It contrasts AIMC's efficiency with traditional digital computing's limitations in power, speed, and scalability. Our comprehensive evaluation focuses on brain tumor analysis, spleen segmentation, and nuclei detection. The study highlights the superior robustness of isotropic architectures, which exhibit a minimal accuracy drop (0.04) in analog-aware training, compared …

abstract ai analysis analog analysis arxiv computing cs.cv cs.ne digital edge eess.iv efficiency enabling imaging in-memory in-memory computing limitations medical medical ai medical imaging memory paradigm power quantification role scalability segmentation speed the edge type uncertainty work