3 research outputs found
Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-Septic Patients
Sepsis is a life-threatening disease and one of the major causes of death in
hospitals. Imaging of microcirculatory dysfunction is a promising approach for
automated diagnosis of sepsis. We report a machine learning classifier capable
of distinguishing non-septic and septic images from dark field microcirculation
videos of patients. The classifier achieves an accuracy of 89.45%. The area
under the receiver operating characteristics of the classifier was 0.92, the
precision was 0.92 and the recall was 0.84. Codes representing the learned
feature space of trained classifier were visualized using t-SNE embedding and
were separable and distinguished between images from critically ill and
non-septic patients. Using an unsupervised convolutional autoencoder,
independent of the clinical diagnosis, we also report clustering of learned
features from a compressed representation associated with healthy images and
those with microcirculatory dysfunction. The feature space used by our trained
classifier to distinguish between images from septic and non-septic patients
has potential diagnostic application.Comment: Accepted for publication at 2018 IEEE International Conference on
Machine Learning and Applications (IEEE ICMLA
CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning
Abstract 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 do 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