Dermatology

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection.

The identification of lesion within medical image data is necessary for diagnosis, treatment and pro...

Improved skin lesions detection using color space and artificial intelligence techniques.

Automatic skin lesion image identification is of utmost importance to develop a fully automatized c...

Deep Learning and Mathematical Models in Dermatology.

Breast Reconstruction Utilizing Buried Dermato-cutaneous Skin Flaps and Immediate Adipocyte Transfer: A Minimally Invasive Autologous Breast Reconstruction Technique.

UNLABELLED: Autologous breast reconstruction historically required flaps that were invasive, require...

Impact of galactooligosaccharides delivered in ovo on mitigating negative effects of heat stress on performance and welfare of broilers.

Galactooligosaccharides (GOS) delivered in ovo improve intestinal health of broiler chickens. This s...

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. R...

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.

INTRODUCTION: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclero...

Diagnostic Value of Lesion-specific Measurement of Myocardial Blood Flow Using Hybrid PET/CT.

BACKGROUND: We evaluated whether lesion-specific measurement of myocardial blood flow (MBF) and flow...

A Machine-Learning Approach to Identify a Prognostic Cytokine Signature That Is Associated With Nivolumab Clearance in Patients With Advanced Melanoma.

Lower clearance of immune checkpoint inhibitors is a predictor of improved overall survival (OS) in ...

Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological a...

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.

The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients ...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke surv...

Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

PURPOSE: Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore...

Skin lesion segmentation using high-resolution convolutional neural network.

BACKGROUND AND OBJECTIVE: Skin lesion segmentation is an important but challenging task in computer-...

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass...

Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.

PURPOSE: Distinguishing benign nevi from malignant melanoma using current histopathological criteria...

Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automati...

Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease.

Deep learning is one of the most exciting new areas in medical imaging. This review article provides...

Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening.

In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multipar...

Deep segmentation networks predict survival of non-small cell lung cancer.

Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is t...

Energy enhanced tissue texture in spectral computed tomography for lesion classification.

Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the ...