AIMC Topic: Skin Neoplasms

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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 ...

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...

Reframing robotics in Mohs surgery for rare cutaneous sarcomas: conceptual promise and clinical realities in precision oncology.

Journal of robotic surgery
Dermatofibrosarcoma protuberans (DFSP) is a rare, locally aggressive cutaneous sarcoma in which achieving histologically negative margins is paramount to minimizing recurrence. Mohs micrographic surgery (MMS) has transformed margin control in DFSP by...

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,...

DeepHybrid-CNN: A hybrid approach for pre-processing of skin cancer images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the current technological era, digital imaging is ubiquitous, and it serves a crucial purpose in the realm of medical research. Skin cancer is one of the most common types of cancer, and its early diagnosis is essential to reduce the mortality rat...

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 ...

Clinician Perspectives of a Magnetic Resonance Imaging-Based 3D Volumetric Analysis Tool for Neurofibromatosis Type 2-Related Schwannomatosis: Qualitative Pilot Study.

JMIR human factors
BACKGROUND: Accurate monitoring of tumor progression is crucial for optimizing outcomes in neurofibromatosis type 2-related schwannomatosis. Standard 2D linear analysis on magnetic resonance imaging is less accurate than 3D volumetric analysis, but s...

Classification of skin diseases with deep learning based approaches.

Scientific reports
Skin diseases are one of the most common health problems that affect people of all ages around the world and significantly reduce the quality of life of individuals. Diseases of eczema, seborrheic dermatitis and skin cancer need to be diagnosed and c...

AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost.

PloS one
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...

Type of pre-existing chronic conditions and their associations with Merkel cell carcinoma (MCC) treatment: Prediction and interpretation using machine learning methods.

PloS one
OBJECTIVE: This study examined the prevalence of pre-existing chronic conditions and their association with the receipt of specific cancer-directed treatments among older adults with incident primary Merkel Cell Carcinoma (MCC) using novel predictive...