Dermatology

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 color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to comple...

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 the most dangerous types of skin cancer, and its survival rate is 5%. The development of skin cancer has risen over the last couple of years. Early identification of skin cancer can help reduce the human mortality rate. Dermoscopy is a technology used for the acquisition of skin images. However, the...

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

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to s...

ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation.

Accurate and reliable segmentation of colorectal tumors and surrounding colorectal tissues on 3D magnetic resonance images has critical importance in ...

Robotic Extended Ultrasound-Guided Distal Pancreatectomy for Pancreatic Metastases from Uveal Melanoma.

BACKGROUND: Isolated pancreatic metastasis from melanoma is extremely uncommon and accounts for approximately only 2% of visceral disseminations of me...

DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation.

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions...

An end-to-end approach to segmentation in medical images with CNN and posterior-CRF.

Conditional Random Fields (CRFs) are often used to improve the output of an initial segmentation model, such as a convolutional neural network (CNN). ...

Multi-features extraction based on deep learning for skin lesion classification.

For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial ste...

Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning.

The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patient...

A disease network-based deep learning approach for characterizing melanoma.

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the dis...

Prognostic Value of Vitamin D Serum Levels in Cutaneous Melanoma.

INTRODUCTION: Vitamin D plays a fundamental role in many metabolic pathways, including those involved in cell proliferation and the immune response. S...

A sandwich SERS immunoassay platform based on a single-layer Au-Ag nanobox array substrate for simultaneous detection of SCCA and survivin in serum of patients with cervical lesions.

The evaluation of tumor biomarkers in blood specimens is vital for patients with cervical lesions. Herein, an ultrasensitive surface enhanced Raman sc...

Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data.

The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity i...

Adaptive Localizing Region-Based Level Set for Segmentation of Maxillary Sinus Based on Convolutional Neural Networks.

In this paper, we propose a novel method, an adaptive localizing region-based level set using convolutional neural network, for improving performance ...

Characteristics of publicly available skin cancer image datasets: a systematic review.

Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total numb...

Machine learning random forest for predicting oncosomatic variant NGS analysis.

Since 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable ti...

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subt...

Dermoscopic image retrieval based on rotation-invariance deep hashing.

Dermoscopic image retrieval technology can provide dermatologists with valuable information such as similar confirmed skin disease cases and diagnosis...

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist i...