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Related Experiment Video

Updated: May 22, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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MedMambaLite-v2: Shared Selective Scan for Efficient Edge Medical Mamba.

Romina Aalishah, Mozhgan Navardi, Nithin Kidangazhiath Mana

    IEEE Transactions on Biomedical Circuits and Systems
    |May 20, 2026
    PubMed
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    MedMambaLite-v2 accelerates AI medical imaging by optimizing State-Space Models (SSMs) for edge devices. This efficient model achieves significant size reduction and energy savings with minimal accuracy loss, enabling real-time diagnostics.

    Area of Science:

    • Artificial Intelligence in Medicine
    • Medical Imaging Analysis
    • Edge Computing for Healthcare

    Background:

    • AI-powered medical imaging supports clinical diagnosis, but State-Space Models (SSMs) like Mamba face deployment challenges due to computational complexity.
    • Real-time and energy-efficient edge applications are limited by the sequential data flow and high resource demands of existing SSMs.

    Purpose of the Study:

    • To propose MedMambaLite-v2, a novel shared selective scan framework for accelerating SSMs on embedded edge platforms.
    • To enhance computational efficiency and reduce model size for real-time medical image analysis at the edge.

    Main Methods:

    • Developed MedMambaLite-v2 with a channel-only transition mechanism and optimized Convolution (Conv) branch.
    • Applied knowledge distillation for model compression, creating a smaller student model.

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  • Designed LiteSS2D hardware with parallelism and 8-bit quantization for efficient inference.

    • Main Results:

    • MedMambaLite-v2 is 23× smaller than the MedMamba baseline with only 1.1% accuracy reduction across 10 MedMNIST datasets.
    • The LiteSS2D hardware prototype shows a 9× latency reduction compared to a serial baseline.
    • Up to 63% and 78% energy reduction per inference on NVIDIA Jetson Orin Nano and Raspberry Pi 5, respectively.

    Conclusions:

    • MedMambaLite-v2 offers a highly efficient solution for deploying advanced AI medical imaging models on edge devices.
    • The framework enables real-time, energy-efficient medical image classification with minimal impact on diagnostic accuracy.
    • This work facilitates the integration of powerful AI tools into clinical workflows at the point of care.