AIMC Topic: Skin Neoplasms

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A multimodal skin lesion classification through cross-attention fusion and collaborative edge computing.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Skin cancer is a significant global health concern requiring early and accurate diagnosis to improve patient outcomes. While deep learning-based computer-aided diagnosis (CAD) systems have emerged as effective diagnostic support tools, they often fac...

Enhancing and advancements in deep learning for melanoma detection: A comprehensive review.

Computers in biology and medicine
Melanoma, although not the most common skin cancer, poses a significant global health challenge, particularly in Europe, where incidence rates are high. Traditional melanoma diagnosis through biopsies can be invasive, but advancements in artificial i...

Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: One of the most promising and rapidly advancing research areas in recent years is using dermoscopic images for automatic diagnosis with artificial intelligence and machine learning methods.

Lesion boundary detection for skin lesion segmentation based on boundary sensing and CNN-transformer fusion networks.

Artificial intelligence in medicine
Traditional convolutional neural networks often struggle to capture global information and handle ambiguous boundaries during complex skin lesion segmentation tasks. To tackle this challenge, we proposed MPBA-Net, a hybrid network that integrates mul...

Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.

PloS one
Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infections, physical activity, exposure to UV light, viral infections, and the effects of climate change. ...

Generating dermatopathology reports from gigapixel whole slide images with HistoGPT.

Nature communications
Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consumin...

Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks.

Scientific reports
Skin cancer is one of the most prevalent malignant tumors, and early detection is crucial for patient prognosis, leading to the development of mobile applications as screening tools. Recent advances in deep neural networks (DNNs) have accelerated the...

Updated Techniques for Melanoma Diagnosis.

Dermatologic clinics
Melanoma, an aggressive skin cancer, requires timely diagnostics for improved patient outcomes. The ABCDE criteria-assessing asymmetry, borders, color, diameter, and evolution-serve as foundational guidelines for early detection. Non-invasive tools l...

Global burden of non-melanoma skin cancers among older adults: a comprehensive analysis using machine learning approaches.

Scientific reports
Non-melanoma skin cancers (NMSCs), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), have shown significant global increases in burden, particularly among older adults, with wide regional, gender, and socio-demographic dispariti...

Impact of fine-tuning parameters of convolutional neural network for skin cancer detection.

Scientific reports
Melanoma skin cancer is a deadly disease with a high mortality rate. A prompt diagnosis can aid in the treatment of the disease and potentially save the patient's life. Artificial intelligence methods can help diagnose cancer at a rapid speed. The li...