Dermatology

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke b...

Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.

Acute radiation dermatitis (ARD) is a common and distressing issue for cancer patients undergoing ra...

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.

Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure...

Deep Location Soft-Embedding-Based Network With Regional Scoring for Mammogram Classification.

Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammo...

ConvNext Mitosis Identification-You Only Look Once (CNMI-YOLO): Domain Adaptive and Robust Mitosis Identification in Digital Pathology.

In digital pathology, accurate mitosis detection in histopathological images is critical for cancer ...

Grading facial aging: Comparing the clinical assessments made by three dermatologists with those obtained by an AI-based scoring system that analyses selfie pictures. A focus on Chinese subjects of both genders.

OBJECTIVE: The objective of this study is to assess the correspondence, in live conditions, between ...

Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.

BACKGROUND: The broad histomorphological spectrum of melanocytic pathologies requires large data set...

Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features.

The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing...

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.

Recently, various modern experimental screening pipelines and assays have been developed to find pro...

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception an...

Artificial intelligence in cosmetic dermatology.

BACKGROUND: Cosmetic dermatology is a growing field as more patients are seeking treatments for esth...

A 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases.

Dedicated brain imaging for cancer patients is seldom recommended in the absence of symptoms. There ...

Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.

This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplement...

Polar contrast attention and skip cross-channel aggregation for efficient learning in U-Net.

The performance of existing lesion semantic segmentation models has shown a steady improvement with ...

Automated identification of fleck lesions in Stargardt disease using deep learning enhances lesion detection sensitivity and enables morphometric analysis of flecks.

PURPOSE: To classify fleck lesions and assess artificial intelligence (AI) in identifying flecks in ...

MASDF-Net: A Multi-Attention Codec Network with Selective and Dynamic Fusion for Skin Lesion Segmentation.

Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermat...

Commentary on "Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis" and "Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis".

Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new s...

Semantic segmentation in skin surface microscopic images with artifacts removal.

Skin surface imaging has been used to examine skin lesions with a microscope for over a century and ...

Natural language processing in dermatology: A systematic literature review and state of the art.

BACKGROUND: Natural Language Processing (NLP) is a field of both computational linguistics and artif...

EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation.

Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative an...