Search results for "Machine learning"
showing 10 items of 1464 documents
Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricu…
2019
Background: Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms. Methods: Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute–right ventricular fail…
Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning …
2021
Objective: The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging. Material and methods: Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been impl…
Computer-Aided Detection for Prostate Cancer Detection based on Multi-Parametric Magnetic Resonance Imaging
2017
International audience; Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer…
Choline PET/CT Features to Predict Survival Outcome in High Risk Prostate Cancer Restaging: A Preliminary Machine-Learning Radiomics Study
2020
Background Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18F-Cho PET/CT imaging features to predict disease progression in PCa. Methods We retrospectively analyzed high-risk PCa patients who underwent restaging 18F-Cho PET/CT from November 2013 to May 2018. 18F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model h…
Prognostic Impact of Frozen Section Investigation and Extent of Proximal Safety Margin in Gastric Cancer Resection
2020
Background and aims Guidelines propose different extents of macroscopic proximal margin for gastric cancer and frozen margin investigation in selected cases, but data is lacking. This study was to evaluate the necessary extent of macroscopic proximal margin, accuracy of frozen margin investigation, and prognostic impact of tumor-free proximal margin length in pT2-pT4 gastric cancer. Study design Proximal and distal frozen margins were routinely investigated intraoperatively in all pT2-pT4 gastric cancers resected between 2011 and 2017. Macroscopic and microscopic proximal margin lengths were correlated. For R0-resections, survival analysis was performed for distal gastrectomy (DG) with micr…
Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.
2020
Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and &ldquo
Statistical formats to optimize evidence-based decision making: A behavioral approach
2013
Abstract Statistical information is crucial for managerial decision making. The decision-making literature in psychology and mathematical cognition documents how different statistical formats can facilitate certain types of decisions. The present analysis is the first of its kind to assess the impact of statistical formats in the presentation of data from market research on both the optimality of market decisions and the time required to perform the decision-making process. An economic experiment provides the data for this study. The experiment presents statistical information in simple frequencies and relative frequencies using numerical and pictorial representations in the context of diff…
A New Tool for the Modeling of AI and Machine Learning Applications: Random Walk-Jump Processes
2011
Published version of an article from the book: Hybrid artificial intelligent systems, Lecture notes in computer science. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-21219-2_2 There are numerous applications in Artificial Intelligence (AI) and Machine Learning (ML) where the criteria for decisions are based on testing procedures. The most common tools used in such random phenomena involve Random Walks (RWs). The theory of RWs and its applications have gained an increasing research interest since the start of the last century. [1]. In this context, we note that a RW is, usually, defined as a trajectory involving a series of successive ran…
Convolutional Neural Networks for Multispectral Image Cloud Masking
2020
Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems. One of their major strengths is that, when enough data is available, CNN perform an end-to-end learning without the need of custom feature extraction methods. In this work, we study the use of different CNN architectures for cloud masking of Proba-V multispectral images. We compare such methods with the more classical machine learning approach based on feature extraction plus supervised classification. Experimental results suggest that CNN are a promising alternative for solving cloud masking problems.
Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification
2021
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractical to build a large homogeneous annotated dataset for a specific pathology. Self-supervised methods offer a new way to learn a representation of the images in an unsupervised manner with a neural network. In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks. Nonetheless, this method does not take advantage of available meta-data, such as participant’s age, viewed as prior knowledge. Here, we propose to leverage continuous proxy me…