The current classification of cutaneous melanoma was developed in 1972 and revised in 1982. Since that time new concepts and terminology have evolved that require consideration of a further revision. This paper reviews some of the concepts that will form part of that process. Regional meetings of interested parties have been held to review the classification and there will be an open meeting on the topic at the 1997, 4th World Conference on Melanoma in Sydney, Australia. A questionnaire is included that will allow the interested reader to provide comments on the topic.
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http://dx.doi.org/10.1097/00008390-199702000-00002 | DOI Listing |
NPJ Digit Med
January 2025
AIM for Health Lab, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
Traditional disease classification models often disregard the clinical significance of misclassifications and lack interpretability. To overcome these challenges, we propose a hierarchical prototypical decision tree (HPDT) for skin lesion classification. HPDT combines prototypical networks and decision trees, leveraging a class hierarchy to guide interpretable predictions from general to specific categories.
View Article and Find Full Text PDFOphthalmol Sci
November 2024
Liverpool Ocular Oncology Research Group, Department of Eye and Vision Science, Institute of Life Course and Medical Sciences (ILCaMS), University of Liverpool, Liverpool, United Kingdom.
Purpose: Testing the validity of a self-supervised deep learning (DL) model, RETFound, for use on posterior uveal (choroidal) melanoma (UM) and nevus differentiation.
Design: Case-control study.
Subjects: Ultrawidefield fundoscopy images, both color and autofluorescence, were used for this study, obtained from 4255 patients seen at the Liverpool Ocular Oncology Center between 1995 and 2020.
Cancers (Basel)
January 2025
Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320, Taiwan.
Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions.
View Article and Find Full Text PDFCancers (Basel)
December 2024
65+ Outpatient Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece.
: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA.
Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption.
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