AIMC Topic:
Image Interpretation, Computer-Assisted
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Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks.
IEEE journal of biomedical and health informatics
Dec 25, 2017
Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is ...
A deep learning method for classifying mammographic breast density categories.
Medical physics
Dec 22, 2017
PURPOSE: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density...
Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study.
Schizophrenia research
Dec 21, 2017
Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validat...
Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks.
IEEE transactions on cybernetics
Dec 20, 2017
Segmenting human left ventricle (LV) in magnetic resonance imaging images and calculating its volume are important for diagnosing cardiac diseases. The latter task became the topic of the Second Annual Data Science Bowl organized by Kaggle. The datas...
Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI.
IEEE transactions on bio-medical engineering
Dec 20, 2017
OBJECTIVE: Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurr...
Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.
Medical image analysis
Dec 20, 2017
Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially importa...
Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis.
Scientific reports
Dec 20, 2017
Current cancer diagnosis employs various nuclear morphometric measures. While these have allowed accurate late-stage prognosis, early diagnosis is still a major challenge. Recent evidence highlights the importance of alterations in mechanical propert...
Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.
Journal of neuroscience methods
Dec 18, 2017
BACKGROUND: In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no stud...
Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.
Computer methods and programs in biomedicine
Dec 16, 2017
Background and Objective Fatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratificatio...
Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 11, 2017
Cancer cell detection and its stages recognition of life cycle are an important step to analyze cellular dynamics in the automation of cell based-experiments. In this work, a two-stage hierarchical method is proposed to detect and recognize different...
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