This study explores Analog In-memory Computing (AIMC) in medical AI analysis, emphasizing its efficiency over traditional digital computing. The research evaluates brain tumor analysis, spleen segmentation, and nuclei detection, highlighting the robustness of isotropic architectures in analog-aware training. AIMC's data pipelining reduces latency and increases throughput while leveraging inherent noise to enhance model certainty. Noise-resilient Swin-like transformer architectures outperform pyramidal structures in stability against noise disturbances. Performance metrics show significant improvements with AIMC in processing MRI and CT images for medical applications. Strategic noise injection during hardware-aware training enhances model precision and diagnostic confidence, crucial for healthcare decision-making.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Imane Hamzao... at arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.08796.pdfDeeper Inquiries