Dermatology

Breast ultrasound lesion classification based on image decomposition and transfer learning.

PURPOSE: In medical image analysis, deep learning has great application potential. Discovering a method for extracting valuable information from medical images and integrating that information closely with medical treatment has recently become a major topic of interest. Because obtaining large volumes of breast lesion ultrasound image data is difficult, transfer learning is usually employed to obt...

Lesion-aware convolutional neural network for chest radiograph classification.

AIM: To investigate the performance of a deep-learning approach termed lesion-aware convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X-rays (CXRs).

Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging.

We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Est...

Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.

In the dermoscopic diagnosis of skin tumors, it remains unclear whether a deep neural network (DNN) trained with images from fair-skinned-predominant ...

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

OBJECTIVES: There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study ...

A Point-of-Care, Real-Time Artificial Intelligence System to Support Clinician Diagnosis of a Wide Range of Skin Diseases.

Dermatological diagnosis remains challenging for nonspecialists because the morphologies of primary skin lesions widely vary from patient to patient. ...

Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss.

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we p...

Performance of a deep learning-based application for the diagnosis of basal cell carcinoma in Indian patients as compared to dermatologists and nondermatologists.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for ...

Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement.

OBJECTIVE: We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc...

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the es...

Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committe...

Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts.

BACKGROUND AND OBJECTIVES: One of the challenges in developing effective hair loss therapies is the lack of reliable methods to monitor treatment resp...

Review of medical image recognition technologies to detect melanomas using neural networks.

BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization e...

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of art...

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strat...

Value of MR-based radiomics in differentiating uveal melanoma from other intraocular masses in adults.

PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics analysis for discriminating between uveal...

The Vascular Basis of the Pronator Quadratus Muscle Flap and Its Use in Clinical Cases.

 Pronator quadratus (PQ) is a deeply situated muscle in the forearm which may occasionally be utilized for soft-tissue reconstruction. The purpose of...

Impact of tea leaves types on antioxidant properties and bioaccessibility of kombucha.

Five different tea varieties (white, green, oolong, black and pu-erh) were infused, drained and used for kombucha production. Antioxidant capacity, to...

Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

BACKGROUND: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Monosomy 3 and mutation are strong prognostic factors pr...