Dermatology

A network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia of pantomime.

Neurological patients with apraxia of pantomime provide us with a unique opportunity to study the neural correlates of higher-order motor function. Previous studies using lesion-behaviour mapping methods led to inconsistent anatomical results, reporting various lesion locations to induce this symptom. We hypothesised that the inconsistencies might arise from limitations of mass-univariate lesion-b...

Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.

IMPORTANCE: A high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and pathology services.

Automated grading of acne vulgaris by deep learning with convolutional neural networks.

BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variab...

Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification.

Deep learning has been used to analyze and diagnose various skin diseases through medical imaging. However, recent researches show that a well-trained...

A superpixel-driven deep learning approach for the analysis of dermatological wounds.

BACKGROUND: The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive eva...

Technical considerations of multi-parametric tissue outcome prediction methods in acute ischemic stroke patients.

Decisions regarding acute stroke treatment rely heavily on imaging, but interpretation can be difficult for physicians. Machine learning methods can a...

Superior skin cancer classification by the combination of human and artificial intelligence.

BACKGROUND: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and n...

Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs.

We developed an artificial intelligence algorithm (AIA) for smartphones to determine the severity of facial acne using the GEA scale and to identify d...

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

Deep learning networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets, particul...

Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks.

Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the lo...

Gabor wavelet-based deep learning for skin lesion classification.

Skin cancer cases are increasing and becoming one of the main problems worldwide. Skin cancer is known as a malignant type of skin lesion, and early d...

Deep Learning to Improve Breast Cancer Detection on Screening Mammography.

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging probl...

Identifying Phenotypes of Atopic Dermatitis in a Longitudinal United States Cohort Using Unbiased Statistical Clustering.

Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security.

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport thro...

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi ...

Cross-registry neural domain adaptation to extract mutational test results from pathology reports.

OBJECTIVE: We study the performance of machine learning (ML) methods, including neural networks (NNs), to extract mutational test results from patholo...

Deep neural networks are superior to dermatologists in melanoma image classification.

BACKGROUND: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial inte...

Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data.

Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30-50% of all melan...

Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, m...

Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy.

Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to preci...