Dermatology

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization.

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused comp...

Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.

Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in d...

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks.

Recent years have seen a steep rise in the number of skin cancer detection applications. While moder...

Updates in Artificial Intelligence for Breast Imaging.

Artificial intelligence (AI) for breast imaging has rapidly moved from the experimental to implement...

Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image.

Deep learning methods, especially convolutional neural networks, have advanced the breast lesion cla...

Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.

The mitotic count (MC) is an important histological parameter for prognostication of malignant neopl...

Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach.

The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagno...

Perceptions of the use of artificial intelligence in the diagnosis of skin cancer: an outpatient survey.

BACKGROUND: Convolutional neural networks (artificial intelligence, AI) are rapidly appearing within...

A Prediction Model to Discriminate Small Choroidal Melanoma from Choroidal Nevus.

OBJECTIVE: This study aimed to develop a validated machine learning model to diagnose small choroida...

Machine Learning and Its Application in Skin Cancer.

Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine lea...

Residual refinement for interactive skin lesion segmentation.

BACKGROUND: Image segmentation is a difficult and classic problem. It has a wide range of applicatio...

The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

OBJECTIVES: The aim of this study was to investigate the detection efficacy of deep learning (DL) fo...

Quantitative analysis of fragrance allergens in various matrixes of cosmetics by liquid-liquid extraction and GC-MS.

Fragrances are the most common chemicals in cosmetics to which people expose every day. However, the...

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials.

Background Deep learning-based segmentation could facilitate rapid and reproducible T1 lesion load a...

Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia.

Upper gastrointestinal (GI) neoplasia account for 35% of GI cancers and 1.5 million cancer-related d...

Hyper-fusion network for semi-automatic segmentation of skin lesions.

Segmentation of skin lesions is an important step for imaging-based clinical decision support system...

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets.

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Hand...

Reconstruction of Scalp Anterior Hairline with Tissue Expansion and Skin Flap.

INTRODUCTION: Cutaneous defects involving the frontal region and anterior hairline of the scalp can ...

Deep Learning-Based Transfer Learning for Classification of Skin Cancer.

One of the major health concerns for human society is skin cancer. When the pigments producing skin ...

A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification.

In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of ...

Artificial intelligence for grading in acne vulgaris: current situation and prospect.