AIMC Topic: Skin Neoplasms

Clear Filters Showing 431 to 440 of 540 articles

Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND/PURPOSE: Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature ...

Four-class classification of skin lesions with task decomposition strategy.

IEEE transactions on bio-medical engineering
This paper proposes a new computer-aided method for the skin lesion classification applicable to both melanocytic skin lesions (MSLs) and nonmelanocytic skin lesions (NoMSLs). The computer-aided skin lesion classification has drawn attention as an ai...

P22 Using VECTRA and AI analysis to monitor paediatric lesions: a review of cases.

The British journal of dermatology
BACKGROUND: Paediatric melanoma is a rare but important diagnosis. In the paediatric cohort, diagnostic challenges arise due to lesion variability and the inherent difficulties associated with paediatric assessment. Clinical decision-making is furthe...

Early diagnosis model of mycosis fungoides and five inflammatory skin diseases based on a multimodal data-based convolutional neural network.

The British journal of dermatology
BACKGROUND: Mycosis fungoides (MF) is the most common type of cutaneous T-cell lymphoma, and early-stage MF is difficult to differentiate from erythematous inflammatory disease. With the exception of biopsy, noninvasive information such as a patient'...

Artificial Intelligence in Dermatology: A Comprehensive Review of Approved Applications, Clinical Implementation, and Future Directions.

International journal of dermatology
This comprehensive review examines artificial intelligence (AI) applications in dermatology, approved by the United States (U.S.) Food and Drug Administration (FDA) and international organizations, evaluating their clinical implementation and impact ...

The Development and Evaluation of a Convolutional Neural Network for Cutaneous Melanoma Detection in Whole Slide Images.

Archives of pathology & laboratory medicine
CONTEXT.—: The current melanoma staging system does not account for 26% of the variance seen in melanoma-specific survival, therefore our ability to predict patient outcome is not fully elucidated. Morphology may be of greater significance than in ot...

The melanoma MEGA-study: Integrating proteogenomics, digital pathology, and AI-analytics for precision oncology.

Journal of proteomics
Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to conventional therapies. The Melanoma MEGA-Study is a multi-center initiative designed to address these clin...

Development and Evaluation of Natural Language Processing Methods for Extracting Key Melanoma Pathology Concepts.

Studies in health technology and informatics
This study presents the development and evaluation of an annotation schema and rule-based natural language processing (NLP) system for extracting key melanoma pathology concepts from surgical pathology reports. Achieving high precision and recall, ou...

Nomograms versus artificial intelligence platforms: which one can better predict sentinel node positivity in melanoma patients?

Melanoma research
Nomograms are commonly used in oncology to assist clinicians in individualized decision-making processes, such as considering sentinel node biopsy (SNB) for melanoma patients. Concurrently, artificial intelligence (AI) is increasingly being utilized ...

Deep pixel-wise supervision for skin lesion classification.

Computers in biology and medicine
BACKGROUND: Utilizing automated systems for diagnosing malignant skin lesions promises to improve the early detection of skin diseases and increase patients' survival rates. However, current classification methods primarily focus on global features, ...