AIMC Topic: Skin Diseases

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Closing the AI generalisation gap by adjusting for dermatology condition distribution differences across clinical settings.

EBioMedicine
BACKGROUND: Generalisation of artificial intelligence (AI) models to a new setting is challenging. In this study, we seek to understand the robustness of a dermatology (AI) model and whether it generalises from telemedicine cases to a new setting inc...

An attention based hybrid approach using CNN and BiLSTM for improved skin lesion classification.

Scientific reports
Skin lesions remain a significant global health issue, with their incidence rising steadily over the past few years. Early and accurate detection is crucial for effective treatment and improving patient outcomes. This work explores the integration of...

Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning.

Journal of medical systems
Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient visits is essential for optimizing hospital resources and improving diagnosis and treatment methods. Ba...

Recent advances in cutaneous drug delivery by iontophoresis.

Expert opinion on drug delivery
INTRODUCTION: Iontophoresis has been extensively studied for topical and transdermal drug delivery to stimulate the absorption of molecules that would hardly pass through the outermost layer of the skin passively. Recent research has focused on its c...

Evaluating medical AI systems in dermatology under uncertain ground truth.

Medical image analysis
For safety, medical AI systems undergo thorough evaluations before deployment, validating their predictions against a ground truth which is assumed to be fixed and certain. However, in medical applications, this ground truth is often curated in the f...

Enhancing skin disease classification leveraging transformer-based deep learning architectures and explainable AI.

Computers in biology and medicine
Skin diseases affect over a third of the global population, yet their impact is often underestimated. Automating the classification of these diseases is essential for supporting timely and accurate diagnoses. This study leverages Vision Transformers,...

FEGGNN: Feature-Enhanced Gated Graph Neural Network for robust few-shot skin disease classification.

Computers in biology and medicine
Accurate and timely classification of skin diseases is essential for effective dermatological diagnosis. However, the limited availability of annotated images, particularly for rare or novel conditions, poses a significant challenge. Although few-sho...

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution.

Scientific reports
Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentation aids in identifying and localizing diseases, monitoring morphological changes, and extracting features for further diagnosis, especially in the ea...

Predictive modeling and optimization in dermatology: Machine learning for skin disease classification.

Computers in biology and medicine
The accurate diagnosis of skin diseases is crucial for effective patient management and treatment, yet traditional diagnostic methods often involve subjective interpretation and can lead to variability in outcomes. In this study, we harness the power...