AIMC Topic: Image Interpretation, Computer-Assisted

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Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.

PloS one
BACKGROUND: In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. However, these CNNs failed to outperform the International Symposiu...

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm.

Human brain mapping
Imaging studies have characterized functional and structural brain abnormalities in adults after premature birth, but these investigations have mostly used univariate methods that do not account for hypothesized interdependencies between brain region...

Application of Deep Learning in Quantitative Analysis of 2-Dimensional Ultrasound Imaging of Nonalcoholic Fatty Liver Disease.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To verify the value of deep learning in diagnosing nonalcoholic fatty liver disease (NAFLD) by comparing 3 image-processing techniques.

Role of deep learning in infant brain MRI analysis.

Magnetic resonance imaging
Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomo...

Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction.

NeuroImage
The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing choices, such as the parcellation scheme used to define regions of interest (ROIs). In this study, we critically evaluate the effect of bra...

Self-supervised iterative refinement learning for macular OCT volumetric data classification.

Computers in biology and medicine
We present self-supervised iterative refinement learning (SIRL) as a pipeline to improve a type of macular optical coherence tomography (OCT) volumetric image classification algorithms. In this type of algorithms, first, two-dimensional (2D) image cl...

Automatic CIN Grades Prediction of Sequential Cervigram Image Using LSTM With Multistate CNN Features.

IEEE journal of biomedical and health informatics
Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, colposcopy is an indispensable part of screening for cervical intraepithelial neoplasia (CIN) grades and cervical cancer but exhibits high misdiagnosis r...

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

European radiology
OBJECTIVE: The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performa...

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.