Latest AI and machine learning research in dermatology for healthcare professionals.
Medical images and radiology reports are crucial for diagnosing medical conditions, highlighting the importance of quantitative analysis for clinical decision-making. However, the diversity and cross-source heterogeneity of these data challenge the generalizability of current data-mining methods. Multimodal large language models (MLLMs) have recently transformed many domains, significantly affec...
Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigatio...
Background Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography re...
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melan...
Skin cancer, one of the most prevalent and life-threatening cancers globally, has become a focus of deep learning applications due to its significant ...
Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effectiv...
Retinal lesion is a cause of age-related macular degeneration that poses a big threat to elderly population. The accurate detection and segmentation o...
Automated diagnosis of eczema from digital camera images is crucial for applications that allow patients to self-monitor their recovery. An important ...
In this work, we assess the impact of self-supervised learning (SSL) approaches on the detection of gastritis atrophy (GA) and intestinal metaplasia (...
Automated cancer diagnosis research often focuses on a binary task - recognize dysplasia and cancer from other lesions. However, other clinical condit...
This study explores the utilization of Dermatoscopic synthetic data generated through stable diffusion models as a strategy for enhancing the robustne...
Detecting and classifying lesions in breast ultrasound images is a promising application of artificial intelligence (AI) for reducing the burden of ...
Background: The existence of different basal cell carcinoma (BCC) clinical criteria cannot be objectively validated. An adequate ground-truth is nee...
Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs ...
BACKGROUND: Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients.
BACKGROUND: The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion ...
BACKGROUND: Patient information leaflets (PILs) can supplement a clinical consultation and provide additional information for a patient to read in the...
BACKGROUND: Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several ...