Dermatology

Melanoma imaging and diagnosis: What does the future hold?

BACKGROUND: In Australia, artificial intelligence (AI) is increasingly being used in the field of me...

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and t...

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images i...

Improving Computer-aided Detection for Digital Breast Tomosynthesis by Incorporating Temporal Change.

Purpose To develop a deep learning algorithm that uses temporal information to improve the performan...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor recept...

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Purpose To determine whether time-dependent deep learning models can outperform single time point mo...

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Advances in general-purpose computers have enabled the generation of high-quality synthetic medical ...

Precise Identification of Oral Cancer Lesions Using Artificial Intelligence.

Dentists, especially those who are not oral lesion specialists and live in rural areas, need an arti...

Patient-led skin cancer teledermatology without dermoscopy during the COVID-19 pandemic: important lessons for the development of future patient-facing teledermatology and artificial intelligence-assisted -self-diagnosis.

MySkinSelfie is a mobile phone application for skin self-monitoring, enabling secure sharing of pati...

Considerations for use of artificial intelligence in the dermatology residency application process.

Machine learning models for differential diagnosing HER2-low breast cancer: A radiomics approach.

To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging...

Accuracy of an Artificial Intelligence System for Interval Breast Cancer Detection at Screening Mammography.

Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, alt...

Segmentation of stroke lesions using transformers-augmented MRI analysis.

Accurate segmentation of chronic stroke lesions from mono-spectral magnetic resonance imaging scans ...

Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.

PURPOSE: Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with n...

Large Language and Vision Assistant in dermatology: a game changer or just hype?

The integration of artificial intelligence (AI) in healthcare, particularly in the field of dermatol...

Practical applications of artificial intelligence in dermatology residency training.

A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases.

BACKGROUND: Artificial intelligence (AI) is reshaping healthcare, using machine and deep learning (D...

TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning.

Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining co...

Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN.

This study explores the utilization of Dermatoscopic synthetic data generated through stable diffusi...

Beyond Dysplasia: Uncovering Structure in Oral Potentially Malignant Diseases with Unsupervised Contrastive Learning.

Automated cancer diagnosis research often focuses on a binary task - recognize dysplasia and cancer ...

Improving Endoscopy Lesion Classification Using Self-Supervised Deep Learning.

In this work, we assess the impact of self-supervised learning (SSL) approaches on the detection of ...