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Neural networks are powerful tools for medical image classification and segmentation. However, existing network structures and training procedures assume that the output classes are mutually exclusive and equally important. Many datasets of medical images do not satisfy these conditions. For example, some skin disease datasets have images labelled as coarse-grained class (such as Benign) in addition...
Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that arises due to development or disease. This renders comparisons between the emerging brain connectivity networks more challenging, since correspondences between their elements are not preserved. Unveiling these correspondences...
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE) and paraseptal emphysema (PSE). Automated classification methods based on supervised learning are generally based upon the current definition of emphysema subtypes, while unsupervised learning...
We consider the problem of domain shift in analyses of brain MRI data. While many different datasets are publicly available, most algorithms are still trained on a single dataset and often suffer the problem of limited and unbalanced sample sizes. In this work, we propose a surprisingly simple strategy to reduce the impact of domain shift - caused by different data sources and processing pipelines...
Autism Spectrum Disorder (ASD) is a complex developmental disorder affecting 1 in 68 children in the United States. While the prevalence may be on the rise, we currently lack a firm understanding of the etiology of the disease, and diagnosis is made purely on behavioral observation and informant report. As one potential method for improving our understanding of ASD, the current study took a network-level...
Multi-atlas based label fusionmethods have been successfully used for medical image segmentation. In the field of brain region segmentation, multi-atlas based methods propagate labels from multiple atlases to target image by the similarity between patches in target image and atlases. Most of existing multi-atlas based methods usually use intensity feature, which is hard to capture high-order information...
The human brain is organized into functionally specialized subnetworks, referred to as modules. Many methods have been employed to detect modules in the brain network, e.g. Newman's modularity and the Louvain method for community detection. However, these methods suffer from a resolution limit, and the detected number of modules is often inaccurate. In this work, we adopt Eigen Value Decomposition...
One of the main challenges of histological image analysis is the high dimensionality of the images. This can be addressed via summarizing techniques or feature engineering. However, such approaches can limit the performance of subsequent machine learning models, particularly when dealing with highly heterogeneous tissue samples. One possible alternative is to employ unsupervised learning to determine...
Automatic identification of side branch and main vascular measurements in IVOCT images take critical roles in pre-interventional decision making for coronary artery disease treatment. Very little works have been presented on these tasks. In this paper, we proposed a novel side branch identification algorithm which utilizes a newly defined global curvature feature to identify the ostium of side branch...
As medical imaging datasets grow, we are approaching the era of big data for radiologic decision support systems. This requires renewed efforts in dataset curation and labeling. We propose a methodology for weak labeling of medical images for attributes such as anatomy and disease that relies on image to sentence transformation. The methodology consists of three models, a convolutional neural network...
Resting-state functional magnetic resonance imaging (rs-fMRI) has become a powerful technique for analyzing cognitive function and its disruption in mental diseases, including schizophrenia. It has played an important role in analyzing and diagnosing mental diseases. In this paper, we present a novel machine learning approach called group-wise sparse representation of rs-fMRI signals to find differences...
Similarity in appearance between various skin diseases, often makes it challenging for clinicians to identify the type of skin condition, and the accuracy is highly reliant on the level of expertise. There is also a great degree of subjectivity and inter/intra observer variability found in the clinical practices. In this paper, we propose a method for automatic skin diseases recognition that combines...
In this study, a novel feature selection framework is proposed to simultaneously perform classification and clinical scores prediction of Parkinson's disease (PD) via multi-modal neuroimaging data. Specifically, a new feature selection model is devised to capture discriminative features to train support vector regression model for clinical scores (e.g., sleep scores and olfactory scores) prediction...
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