Redefining lightweight vision models for healthcare AI

BackgroundVision models for medical imaging often require tens of millions of parameters, raising questions about whether architectural efficiency can be achieved without sacrificing classification performance. We introduce MedLiT-seed (2.1 Million parameters) and MedLiT-nano (0.75 Million parameters), two ultra-lightweight vision transformers designed for efficient and scalable medical image analysis.MethodsMedLiT employs a streamlined Mixture-of-Experts (MoE) architecture with SwiGLU feedforwa