AIMC Topic: Melanoma
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Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach.
Journal of imaging informatics in medicine
Sep 16, 2024
Cutaneous melanoma is a highly lethal form of cancer. Developing a medical image segmentation model capable of accurately delineating melanoma lesions with high robustness and generalization presents a formidable challenge. This study draws inspirati...
An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.
Journal of translational medicine
Sep 12, 2024
BACKGROUND: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end...
Analysis of international publication trends in artificial intelligence in skin cancer.
Clinics in dermatology
Sep 10, 2024
Bibliometric methods were used to analyze publications on the use of artificial intelligence (AI) in skin cancer from 2010 to 2022, aiming to explore current publication trends and future directions. A comprehensive search using four terms, "artifici...
Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.
Scientific reports
Sep 6, 2024
Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for pat...
Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.
Journal of the European Academy of Dermatology and Venereology : JEADV
Aug 31, 2024
BACKGROUND: The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)-based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger...
Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.
European journal of cancer (Oxford, England : 1990)
Aug 25, 2024
IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic cate...
Commentary on "Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis" and "Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis".
Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]
Aug 19, 2024
EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation.
Journal of imaging informatics in medicine
Aug 15, 2024
Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have limitations i...
Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.
Journal of imaging informatics in medicine
Aug 9, 2024
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in wh...
A Self-Supervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 9, 2024
Eyelid malignant melanoma (MM) is a rare disease with high mortality. Accurate diagnosis of such disease is important but challenging. In clinical practice, the diagnosis of MM is currently performed manually by pathologists, which is subjective and ...
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