AIMC Topic: Melanoma

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An advanced skin lesion segmentation and classification framework using deep learning strategies.

Scientific reports
Skin cancer is a deadly kind of cancer that grows rapidly and produces life-threatening issues within six weeks from the initial stage. Accurate analysis is needed for observing both the malignant and benign skin lesions that are more complicated to ...

Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approach.

Scientific reports
This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approac...

Nonmagnetic Biomarker Detection by Nanocatalytic-Amplified Quantum Spin Relaxometry.

ACS nano
Nitrogen-vacancy (NV) centers in diamond demonstrate advantages in biosensing due to their exceptional photostability and long spin coherence time. However, clinical applications of NV centers are significantly limited because their spin states lack ...

Recognition of molecular clusters and a novel prognostic signature based on natural killer cell-related genes in skin cutaneous melanoma.

World journal of surgical oncology
BACKGROUND: Skin cutaneous melanoma (SKCM) is the third most common type of cutaneous malignant tumor with a poor prognosis. This research aimed to recognize molecular clusters and develop a novel prognostic signature based on natural killer (NK) cel...

Semi-supervised GAN with hybrid regularization and evolutionary hyperparameter tuning for accurate melanoma detection.

Scientific reports
Melanoma, influenced by changes in deoxyribonucleic acid (DNA), requires early detection for effective treatment. Traditional melanoma research often employs supervised learning methods, which necessitate large, labeled datasets and are sensitive to ...

An attention-based mRNA transformer network for accurate prediction of melanoma response to immune checkpoint inhibitors.

Scientific reports
Melanoma immunotherapy urgently requires approaches that can accurately predict drug responses to minimize unnecessary treatments. Deep learning models have emerged as powerful tools in this domain due to their robust predictive capabilities. Integra...

A high-resolution, nanopore-based artificial intelligence assay for DNA replication stress in human cancer cells.

Nature communications
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...

Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

European journal of cancer (Oxford, England : 1990)
IMPORTANCE: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learnin...

Advanced skin cancer prediction with medical image data using MobileNetV2 deep learning and optimized techniques.

Scientific reports
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
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 ...