AIMC Topic: Melanoma
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A high-resolution, nanopore-based artificial intelligence assay for DNA replication stress in human cancer cells.
Nature communications
Aug 19, 2025
DNA replication stress is a hallmark of cancer that is exploited by chemotherapies. Current assays for replication stress have low throughput and poor resolution whilst being unable to map the movement of replication forks genome-wide. We present a n...
Advanced skin cancer prediction with medical image data using MobileNetV2 deep learning and optimized techniques.
Scientific reports
Aug 7, 2025
Skin cancer, especially melanoma, has become one of the most widespread and deadly diseases today. The chances of successful treatment are greatly reduced if the melanoma is not treated in its early stages because it could spread aggressively. Hence,...
New Release of User-Captured Images from the Oregon Health & Science University Melanoma MoleMapper Project.
Scientific data
Aug 2, 2025
We announce the release of the OHSU MoleMapper Smartphone Skin Images dataset which contains over six years of new data acquired from the Oregon Health & Science University's (OHSU) MoleMapper study. This released dataset includes 27,499 mole images ...
AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost.
PloS one
Jul 28, 2025
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency...
USP5-Mediated PD-L1 deubiquitination regulates immunotherapy efficacy in melanoma.
Journal of translational medicine
Jul 10, 2025
BACKGROUND: The role of post-translational modifications(PTMs) in PD-L1-mediated immune resistance and melanoma progression remains poorly understood.
Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.
Scientific reports
Jul 10, 2025
Melanoma is the most dangerous type of skin cancer. Although it accounts for only about 1% of all skin cancer cases, it is responsible for the majority of skin cancer-related deaths. Early detection and accurate diagnosis are crucial for improving th...
Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.
Scientific reports
Jul 8, 2025
Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer ...
Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.
Scientific reports
Jul 1, 2025
Cutaneous melanoma is one of the most lethal forms of skin cancer, and its incidence is increasing globally. Its diagnosis typically relies on manual histopathological examination, a process that is both complex and time consuming. In this study, we ...
Automatic melanoma detection using an optimized five-stream convolutional neural network.
Scientific reports
Jul 1, 2025
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...
Urgency vs triage prioritisation: appropriateness of referrer-rated urgency of referrals to a public dermatology service.
The New Zealand medical journal
Jun 27, 2025
AIM: To characterise the appropriateness of community referrer-rated urgency among dermatology referrals.
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