Dermatology

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 tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral computed tomography (CT) is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies. Therefore, this paper aims to address two re...

A multi-context CNN ensemble for small lesion detection.

In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on im...

Mounting Behaviour Recognition for Pigs Based on Deep Learning.

For both pigs in commercial farms and biological experimental pigs at breeding bases, mounting behaviour is likely to cause damage such as epidermal w...

SegAE: Unsupervised white matter lesion segmentation from brain MRIs using a CNN autoencoder.

White matter hyperintensities (WMHs) of presumed vascular origin are frequently observed in magnetic resonance images (MRIs) of the elderly. Detection...

Skin cancer diagnosis based on optimized convolutional neural network.

Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are seve...

Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.

PURPOSE: Accurate lesion segmentation is important for measurements of lesion load and atrophy in subjects with multiple sclerosis (MS). International...

A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features.

In digital mammography, which is used for the early detection of breast tumors, oversight may occur due to overlap between normal tissues and lesions....

Poly(ethylene glycol)-poly(ε-caprolactone)-based micelles for solubilization and tumor-targeted delivery of silibinin.

Silibinin is a naturally occurring compound with known positive impacts on prevention and treatment of many types of in general and cancer in partic...

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study.

BACKGROUND: Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automate...

Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.

BACKGROUND: Psoriasis is associated with elevated risk of heart attack and increased accumulation of subclinical noncalcified coronary burden by coron...

Systematic review of machine learning for diagnosis and prognosis in dermatology.

Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in...

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images.

BACKGROUND: Liver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registrati...

Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

Fixation of intracapsular fracture of the femoral neck using combined peripheral nerve blocks and transthoracic echocardiography in a patient with severe obstructive hypertrophic cardiomyopathy: a case report.

BACKGROUND: Hypertrophic obstructive cardiomyopathy (HOCM) is a type of hypertrophic cardiomyopathy associated with left ventricular outflow tract ste...

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

PURPOSE: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerat...

Smart identification of psoriasis by images using convolutional neural networks: a case study in China.

BACKGROUND: Psoriasis is a chronic inflammatory skin disease, which holds a high incidence in China. However, professional dermatologists who can diag...

Antioxidant, Anti-Melanogenic and Anti-Wrinkle Effects of .

In this study, the antioxidant, anti-xanthine oxidase, anti-melanogenic and anti-wrinkle effects of methanol (ME) and hot water (HE) extracts from the...

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal.

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utili...

Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks.

The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. Howev...

Dense pooling layers in fully convolutional network for skin lesion segmentation.

One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essen...