Search results for "Deep learning"
showing 10 items of 337 documents
A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing
2019
Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…
Markerless 2D kinematic analysis of underwater running : A deep learning approach
2018
Kinematic analysis is often performed with a camera system combined with reflective markers placed over bony landmarks. This method is restrictive (and often expensive), and limits the ability to perform analyses outside of the lab. In the present study, we used a markerless deep learning-based method to perform 2D kinematic analysis of deep water running, a task that poses several challenges to image processing methods. A single GoPro camera recorded sagittal plane lower limb motion. A deep neural network was trained using data from 17 individuals, and then used to predict the locations of markers that approximated joint centres. We found that 300–400 labelled images were sufficient to tra…
Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation
2022
The movement of patients and respiratory motion during MRI acquisition produce image artefacts that reduce the image quality and its diagnostic value. Quality assessment of the images is essential to minimize segmentation errors and avoid wrong clinical decisions in the downstream tasks. In this paper, we propose automatic multi-task learning (MTL) based classification model to detect cardiac MR images with different levels of motion artefact. We also develop an automatic segmentation model that leverages k-space based motion artefact augmentation (MAA) and a novel compound loss that utilizes Dice loss with a polynomial version of cross-entropy loss (PolyLoss) to robustly segment cardiac st…
Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images
2020
Background and objective\ud Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches.\ud \ud Methods\ud We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitu…
De novo liquid biopsy and radio genomic diagnostic approach with combined deep learning artificial neural networks for NSCLC
2022
Each year, the mortality rate and incidence of non-small cell lung cancer (NSCLC) are dramatically increasing. The introduction of liquid biopsy in the clinical practice of NSCLC has completely revolutionized the approach to such neoplasm since is generally detected through complex and invasive procedures and unfortunately at advanced stages. The importance and innovation of liquid biopsy are linked with the possibility of cancer detection at every stage, adjuvant treatment, resistance genotyping, systematic initiation of treatment, minimal residual disease, early detection of relapse, and screening of NSCLC. Circulating tumor DNA (ctDNA) is now emerging as a non-invasive biomarker that wil…
Use of machine learning approaches to improve non-invasive skin melanoma diagnostic method in spectral range 450 - 950nm
2020
Non-invasive skin cancer diagnostic methods develop rapidly thanks to Deep Learning and Convolutional Neural Networks (CNN). Currently, two types of diagnostics are popular: (a) using single image taken under white illumination and (b) using multiple images taken in narrow spectral bands. The first method is easier to implement, but it is limited in accuracy. The second method is more sensitive, because it is possible to use illumination considering the absorption bands of the skin chromophores and the optical properties of the skin. Currently CNN use a single white light image, due to the availability of large datasets with lesion images. Since CNN processing and analysis requires a large …
The effects of tasks on integrating information from multiple documents.
2008
The authors examine 2 issues: (a) how students integrate information from multiple scientific documents to describe and explain a physical phenomenon that represents a subset of the information in the documents; and (b) the role of 2 sorts of tasks to achieve this type of integration, either writing an essay on a question requiring integration across texts or answering shorter intratext questions that require students to integrate information within a single text, while superficial and deep comprehension measurements are obtained. Undergraduate students answered 1 of the 2 types of questions, and their reading times were recorded. Half of the sample thought aloud. Results showed that the in…
A Deep Reinforcement Learning scheme for Battery Energy Management
2020
Deep reinforcement learning is considered promising for many energy cost optimization tasks in smart buildings. How-ever, agent learning, in this context, is sometimes unstable and unpredictable, especially when the environments are complex. In this paper, we examine deep Reinforcement Learning (RL) algorithms developed for game play applied to a battery control task with an energy cost optimization objective. We explore how agent behavior and hyperparameters can be analyzed in a simplified environment with the goal of modifying the algorithms for increased stability. Our modified Deep Deterministic Policy Gradient (DDPG) agent is able to perform consistently close to the optimum over multi…
Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning
2021
Accurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measurements are manually performed by healthcare professionals, being time consuming, highly variable, and suffering from lack of reproducibility. In this work we propose a supervised deep-learning method for the direct estimation of aortic diameters. The approach is devised and tested over 100 magnetic resonance angiography scans without contrast agent. All data was expert-annotated at six aortic locations typically used in clinical practice. Our approach makes use of a 3D+2D convolutional neural network (CNN) that ta…
Learning Automata-Based Solutions to the Multi-Elevator Problem
2019
In the last century, AI has been the topic of interest in many areas, where the focus was on mimicking human behaviour. It has been researched to be incorporated into different domains, such as security, diagnosis, autonomous driving, financial prediction analysis and playing games such as chess and Go. They also worked on different subfields of AI such as machine learning, deep learning, pattern recognition and other relevant subfields. Our work in a previous paper [1] focused on a problem that has not been tackled using AI before, which is the elevator-problem. In which we try to find the optimal parking floor for the elevator for the single elevator problem. In this paper, our work exten…