AIMC Topic: Skin Neoplasms

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Multimodal deep learning ensemble framework for skin cancer detection.

Scientific reports
Skin cancer is the abnormal growth of skin cells, most often developing on skin exposed to the sun. It is among the most fatal forms of cancer, making its early detection and therapy crucial. In addition to conventional techniques, deep learning meth...

3D Total Body Photography as a Promising Innovation for Early Skin Cancer Detection: Scoping Review.

JMIR dermatology
BACKGROUND: Skin cancer (SC) is a global health concern because of its high and still increasing incidence and associated health care cost. Belgium is no exception as 1 in 5 people are diagnosed with SC before the age of 75 years. The VECTRA WB360, a...

Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.

BMC primary care
CONTEXT: Artificial intelligence (AI) technologies are increasingly used for image recognition, especially for skin lesions. Due to what may be long wait times for dermatology appointments, general practitioners (GPs) are the gatekeepers when it come...

Artificial intelligence for skin lesion classification and diagnosis in dermatology: A narrative review.

Medwave
INTRODUCTION: Artificial intelligence (AI) is increasingly present in dermatology, demonstrating accuracy levels comparable to, or even superior to, those of dermatologists in diagnosing skin lesions from clinical and dermoscopic images. This review ...

AttenUNeT X with iterative feedback mechanisms for robust deep learning skin lesion segmentation.

Scientific reports
Accurate skin lesion segmentation is critical for improving early diagnosis of skin cancer. In this study, we propose AttenUNeT X, a novel extension of the U-Net architecture that integrates three key enhancements: (i) a feedback mechanism within dec...

Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.

Nature communications
Accurate prognostication is essential to guide clinical management in localised cutaneous melanoma (CM), the form of skin cancer with the highest mortality. While the tumour microenvironment (TME) plays a key role in disease progression, current stag...

Transformer-aided skin cancer classification using VGG19-based feature encoding.

Scientific reports
Skin cancer is among the most widely distributed, deadliest cancers around the globe, and early diagnosis becomes vital to enhance patient survival. Deep learning has demonstrated high potential for automatic skin lesion classification. However, exis...

Hybrid neurofibroma/schwannoma in schwannomatosis-a diagnostically challenging benign peripheral nerve sheath tumour.

Familial cancer
Hybrid neurofibroma/schwannoma tumors (HNS) represent a still underrecognized, yet clinically and diagnostically significant entity within the spectrum of schwannomatosis (SWN). While classical schwannomas have been well known for decades, HNS have o...

Multi-stage knowledge distillation with layer fusion-based deep learning approach for skin cancer classification.

Scientific reports
Skin cancer is one of the most common types of cancer globally, caused by prolonged exposure to the sun's UV rays. Despite recent developments in research, early diagnosis, prevention, and treatment, skin cancer remains a significant health concern. ...

A methodology for developing dermatological datasets: lessons from retrospective data collection for AI-based applications.

BMC medical research methodology
PURPOSE: The integration of artificial intelligence into dermatological research has underscored the need for robust and well-structured dermatological datasets. However, these datasets vary widely in their development processes, and there is current...