# Efficient Deep Learning for Medical Imaging: Bridging the Gap Between High-Performance AI and Clinical Deployment

arXiv: https://arxiv.org/abs/2602.00910 · HTML: https://mlsanity.com/paper/2602.00910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 65 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 28 of 28)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [MambaOut: Do We Really Need Mamba for Vision?](https://mlsanity.com/paper/2405.07992/) (2024)
- [ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation](https://mlsanity.com/paper/1803.06815/) (2018)
- [Rethinking Vision Transformers for MobileNet Size and Speed](https://mlsanity.com/paper/2212.08059/) (2022)
- [MobileOne: An Improved One millisecond Mobile Backbone](https://mlsanity.com/paper/2206.04040/) (2022)
- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
- [LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.08623/) (2021)
- [LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.05246/) (2024)
- [UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation](https://mlsanity.com/paper/2403.20035/) (2024)
- [Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis](https://mlsanity.com/paper/2203.02202/) (2022)
- [A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond](https://mlsanity.com/paper/2410.02362/) (2024)
- [1M parameters are enough? A lightweight CNN-based model for medical image segmentation](https://mlsanity.com/paper/2306.16103/) (2023)
- [Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis](https://mlsanity.com/paper/2406.03430/) (2024)
- [MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation](https://mlsanity.com/paper/2509.18493/) (2025)
- [ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video](https://mlsanity.com/paper/2207.07759/) (2022)
- [Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2409.11664/) (2024)
- [The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning](https://mlsanity.com/paper/2206.14413/) (2022)
- [HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization](https://mlsanity.com/paper/2407.03307/) (2024)
- [MedMambaLite: Hardware-Aware Mamba for Medical Image Classification](https://mlsanity.com/paper/2508.05049/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis](https://mlsanity.com/paper/2605.30734/) (2026)

## Abstract (arXiv)

Deep learning has revolutionized medical image analysis, playing a vital role in modern clinical applications. However, the deployment of large-scale models in real-world clinical settings remains challenging due to high computational costs, latency constraints, and patient data privacy concerns associated with cloud-based processing. To address these bottlenecks, this review provides a comprehensive synthesis of efficient and lightweight deep learning architectures specifically tailored for the medical domain. We categorize the landscape of modern efficient models into three primary streams: Convolutional Neural Networks (CNNs), Lightweight Transformers, and emerging Linear Complexity Models. Furthermore, we examine key model compression strategies (including pruning, quantization, knowledge distillation, and low-rank factorization) and evaluate their efficacy in maintaining diagnostic performance while reducing hardware requirements. By identifying current limitations and discussing the transition toward on-device intelligence, this review serves as a roadmap for researchers and practitioners aiming to bridge the gap between high-performance AI and resource-constrained clinical environments.