AIMC Topic: Skin Neoplasms

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Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

Nature biomedical engineering Dec 28, 2023
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of exp...
Machine Learning Algorithms Physicians Humans Artificial Intelligence Skin Image Interpretation, Computer-Assisted Dermoscopy Image Processing, Computer-Assisted Melanoma Skin Neoplasms
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SkinViT: A transformer based method for Melanoma and Nonmelanoma classification.

PloS one Dec 27, 2023
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...
Melanoma Humans Skin Neoplasms Artificial Intelligence Dermoscopy Carcinoma, Basal Cell Carcinoma, Squamous Cell
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Bridging AI development with clinical relevance-A scoping review of skin cancer models since CLEAR Derm. Where to next?

The Australasian journal of dermatology Dec 25, 2023
Artificial Intelligence Skin Neoplasms Humans Clinical Relevance Animals Disease Models, Animal
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An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.

Scientific reports Dec 14, 2023
When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherite...
Humans Diagnosis, Computer-Assisted Melanoma Deep Learning Skin Neoplasms Melanocytes
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Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check.

The Journal of investigative dermatology Nov 18, 2023
The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classi...
Skin Image Interpretation, Computer-Assisted Algorithms Skin Neoplasms Humans Artificial Intelligence
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Deep learning based histological classification of adnex tumors.

European journal of cancer (Oxford, England : 1990) Nov 15, 2023
BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant entities occur which can metastasize and lead to patients´ death. Correct diagnosis is critical to ens...
Algorithms Skin Neoplasms Deep Learning Neural Networks, Computer Artificial Intelligence Humans
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A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.

Experimental dermatology Oct 21, 2023
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...
Frozen Sections Mohs Surgery Carcinoma, Squamous Cell Deep Learning Skin Neoplasms Humans Carcinoma, Basal Cell Artificial Intelligence Retrospective Studies
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TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation.

Computers in biology and medicine Oct 21, 2023
Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers' model in the field of computer vision, researchers have begun to gradually explore the appl...
Skin Neoplasms Image Processing, Computer-Assisted Neural Networks, Computer Humans Software Semantics Problem Solving
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Integrated convolutional neural network for skin cancer classification with hair and noise restoration.

Turkish journal of medical sciences Oct 16, 2023
BACKGROUND/AIM: Skin lesions are commonly diagnosed and classified using dermoscopic images. There are many artifacts visible in dermoscopic images, including hair strands, noise, bubbles, blood vessels, poor illumination, and moles. These artifacts ...
Hair Neural Networks, Computer Skin Neoplasms Deep Learning Artifacts Dermoscopy Humans
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A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.

Scientific data Oct 16, 2023
Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of un...
Melanoma Skin Neoplasms Humans Nevus, Epithelioid and Spindle Cell Deep Learning Metadata
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