# Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2605.06714 · HTML: https://mlsanity.com/paper/2605.06714/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 130 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 22 of 22)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks](https://mlsanity.com/paper/1808.06866/) (2018)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution](https://mlsanity.com/paper/1701.00299/) (2017)
- [Bringing AI To Edge: From Deep Learning's Perspective](https://mlsanity.com/paper/2011.14808/) (2020)
- [AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environments](https://mlsanity.com/paper/2303.07129/) (2023)
- [Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI](https://mlsanity.com/paper/2312.14229/) (2023)
- [Generic Feature Learning for Wireless Capsule Endoscopy Analysis](https://mlsanity.com/paper/1607.07604/) (2016)
- [A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications](https://mlsanity.com/paper/2107.06835/) (2021)
- [LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation](https://mlsanity.com/paper/2409.01572/) (2024)
- [Distributed Machine Learning through Heterogeneous Edge Systems](https://mlsanity.com/paper/1911.06949/) (2019)
- [The Benefits of Edge Computing in Healthcare, Smart Cities, and IoT](https://mlsanity.com/paper/2112.01250/) (2021)
- [Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes](https://mlsanity.com/paper/1701.02632/) (2017)
- [Power-Performance Tradeoffs in Data Center Servers: DVFS, CPU pinning, Horizontal, and Vertical Scaling](https://mlsanity.com/paper/1903.05488/) (2019)
- [Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors](https://mlsanity.com/paper/2304.09695/) (2023)
- [YOLOpeds: Efficient Real-Time Single-Shot Pedestrian Detection for Smart Camera Applications](https://mlsanity.com/paper/2007.13404/) (2020)
- [Continual learning for surface defect segmentation by subnetwork creation and selection](https://mlsanity.com/paper/2312.05100/) (2023)

## Built on by (most cited first, 4 of 4)

- [Pixel-Space Diffusion Transformers](https://mlsanity.com/paper/2607.17585/) (2026)
- [Decentralized Direct Volume Rendering: A Browser-Native GPU Architecture for MRI Digital Twins in Resource-Constrained Settings](https://mlsanity.com/paper/2605.19737/) (2026)
- [MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models](https://mlsanity.com/paper/2606.06103/) (2026)
- [TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge](https://mlsanity.com/paper/2608.22716/) (2026)

## Abstract (arXiv)

Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental factors through the close integration of computational resources and data sources. Here we provide a comprehensive review of the current state of the art in edge deep learning, focusing on computer vision applications, in particular medical diagnostics. An overview of the foundational principles and technical advantages of edge deep learning is presented, emphasising the capacity of this technology to revolutionise a wide range of domains. Furthermore, we present a novel categorisation of edge hardware platforms based on performance and usage scenarios, facilitating platform selection and operational effectiveness. Following this, we dive into approaches to effectively implement deep neural networks on edge devices, encompassing methods such as lightweight design and model compression. Reviewing practical applications in the fields of computer vision in general and medical diagnostics in particular, we demonstrate the profound impact edge-deployed deep learning models can have in real-life situations. Finally, we provide an analysis of potential future directions and obstacles to the adoption of edge deep learning, with the intention to stimulate further investigations and advancements of intelligent edge deep learning solutions. This survey provides researchers and practitioners with a comprehensive reference shedding light on the critical role deep learning plays in the advancement of edge computing applications.