AIMC Topic: Skin Neoplasms

Clear Filters Showing 61 to 70 of 504 articles

Melanoma Breslow Thickness Classification Using Ensemble-Based Knowledge Distillation With Semi-Supervised Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Melanoma is considered a global public health challenge and is responsible for more than 90% deaths related to skin cancer. Although the diagnosis of early melanoma is the main goal of dermoscopy, the discrimination between dermoscopic images of in s...

Attention-Guided Learning With Feature Reconstruction for Skin Lesion Diagnosis Using Clinical and Ultrasound Images.

IEEE transactions on medical imaging
Skin lesion is one of the most common diseases, and most categories are highly similar in morphology and appearance. Deep learning models effectively reduce the variability between classes and within classes, and improve diagnostic accuracy. However,...

HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.

Computer methods and programs in biomedicine
Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods...

Fluorescence images of skin lesions and automated diagnosis using convolutional neural networks.

Photodiagnosis and photodynamic therapy
In recent years, interest in applying deep learning (DL) to medical diagnosis has rapidly increased, driven primarily by the development of Convolutional Neural Networks and Transformers. Despite advancements in DL, the automated diagnosis of skin ca...

The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study.

JMIR dermatology
BACKGROUND: Thus far, considerable research has been focused on classifying a lesion as benign or malignant. However, there is a requirement for quick depth estimation of a lesion for the accurate clinical staging of the lesion. The lesion could be m...

Assessment of image quality on the diagnostic performance of clinicians and deep learning models: Cross-sectional comparative reader study.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Skin cancer is a prevalent and clinically significant condition, with early and accurate diagnosis being crucial for improved patient outcomes. Dermoscopy and artificial intelligence (AI) hold promise in enhancing diagnostic accuracy. How...

Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM-ISIC melanoma classification challenge.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: While the high accuracy of reported AI tools for melanoma detection is promising, the lack of holistic consideration of the patient is often criticized. Along with medical history, a dermatologist would also consider intra-patient nevi pa...

Dual scale light weight cross attention transformer for skin lesion classification.

PloS one
Skin cancer is rapidly growing globally. In the past decade, an automated diagnosis system has been developed using image processing and machine learning. The machine learning methods require hand-crafted features, which may affect performance. Recen...