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Carcinoma, Basal Cell

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SkinViT: A transformer based method for Melanoma and Nonmelanoma classification.

PloS one
Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite...

Deep learning-based semantic segmentation of non-melanocytic skin tumors in whole-slide histopathological images.

Experimental dermatology
Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the two most common skin cancer and impose a huge medical burden on society. Histopathological examination based on whole-slide images (WSIs) remains to be the confirmatory diagnostic m...

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.

Experimental dermatology
Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal usi...

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG
BACKGROUND: Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most co...

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteri...

Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.

Journal of imaging informatics in medicine
In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explai...

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.

Physical and engineering sciences in medicine
Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tumoral margins plays a decisive role for a successful result. The aim of this work was to evaluate the...

Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis.

Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]
BACKGROUND: Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of artificial intelligence (AI) in skin cancer diagnosis may alleviate potential care gaps.

Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.

Clinical and experimental dermatology
Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of...