AIMC Topic: Skin Neoplasms
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THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.
Briefings in bioinformatics
Nov 22, 2023
Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option,...
Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things.
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)
Nov 1, 2023
INTRODUCTION: Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and c...
Unveiling the power of convolutional neural networks in melanoma diagnosis.
European journal of dermatology : EJD
Oct 1, 2023
Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, ...
Diagnosis of suspicious pigmented lesions in specialist settings with artificial intelligence.
The Lancet. Digital health
Oct 1, 2023
Key issues when considering adopting a skin cancer diagnostic tool that uses artificial intelligence.
The British journal of dermatology
Jul 17, 2023
Deep learning for Mpox: Advances, challenges, and opportunities.
Med (New York, N.Y.)
May 12, 2023
Although deep-learning algorithms in dermatology have shown promise in diagnosing skin cancers, less is known about potential applications for the diagnosis of infectious diseases. In a recent publication in Nature Medicine, Thieme et al. develop a d...
Updates in Cutaneous Oncology.
Missouri medicine
Jan 1, 2023
Cutaneous oncology is currently a rapidly evolving field. Dermoscopy, total body photography, biomarkers, and artificial intelligence are affecting the way skin cancers, especially melanoma, are diagnosed and monitored. The medical management of loca...
Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.
Journal of biomedical optics
Jun 1, 2022
SIGNIFICANCE: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues struct...
Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review.
The Lancet. Digital health
Jun 1, 2022
Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically reviewed studies on artificial intelligence and machine learning (AI/ML) algorithms that...
Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning.
Cancer control : journal of the Moffitt Cancer Center
Jan 1, 2022
OBJECTIVES: Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts' ex...
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