# CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning

arXiv: https://arxiv.org/abs/2204.08462 · HTML: https://mlsanity.com/paper/2204.08462/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 76 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [TensorFlow-Serving: Flexible, High-Performance ML Serving](https://mlsanity.com/paper/1712.06139/) (2017)
- [Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence](https://mlsanity.com/paper/2002.04803/) (2020)
- [Real-Time Machine Learning: The Missing Pieces](https://mlsanity.com/paper/1703.03924/) (2017)
- [Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-Septic Patients](https://mlsanity.com/paper/1811.02659/) (2018)
- [Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads](https://mlsanity.com/paper/1612.02485/) (2016)
- [A performance comparison of Dask and Apache Spark for data-intensive neuroimaging pipelines](https://mlsanity.com/paper/1907.13030/) (2019)

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

- [Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review](https://mlsanity.com/paper/2205.05493/) (2022)

## Abstract (arXiv)

Recent advances in digital imaging, e.g., increased number of pixels captured, have meant that the volume of data to be processed and analyzed from these images has also increased. Deep learning algorithms are state-of-the-art for analyzing such images, given their high accuracy when trained with a large data volume of data. Nevertheless, such analysis requires considerable computational power, making such algorithms time- and resource-demanding. Such high demands can be met by using third-party cloud service providers. However, analyzing medical images using such services raises several legal and privacy challenges and does not necessarily provide real-time results. This paper provides a computing architecture that locally and in parallel can analyze medical images in real-time using deep learning thus avoiding the legal and privacy challenges stemming from uploading data to a third-party cloud provider. To make local image processing efficient on modern multi-core processors, we utilize parallel execution to offset the resource-intensive demands of deep neural networks. We focus on a specific medical-industrial case study, namely the quantifying of blood vessels in microcirculation images for which we have developed a working system. It is currently used in an industrial, clinical research setting as part of an e-health application. Our results show that our system is approximately 78% faster than its serial system counterpart and 12% faster than a master-slave parallel system architecture.