AIMC Topic: Image Interpretation, Computer-Assisted

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Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge.

Journal of neuroscience methods
BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challen...

Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks.

IEEE journal of biomedical and health informatics
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
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
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
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
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
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
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...

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.

Computer methods and programs in biomedicine
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...