AIMC Topic: Skin Neoplasms

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Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.

The Journal of investigative dermatology Jul 13, 2021
Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the United States. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied ...
Skin Neoplasms Carcinoma, Basal Cell Dermoscopy Reproducibility of Results Humans Artificial Intelligence Female Microscopy, Confocal Biopsy Models, Biological Automation Physical Examination Male Aged Aged, 80 and over Adult Middle Aged Deep Learning
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Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.

Dermatology (Basel, Switzerland) Jul 8, 2021
BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuab...
Humans Neural Networks, Computer Male Middle Aged Imaging, Three-Dimensional Early Detection of Cancer Whole Body Imaging Photography Nevus Female Melanoma Skin Neoplasms Reproducibility of Results Aged Adult Sensitivity and Specificity
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Accuracy of a convolutional neural network for dermatological diagnosis of tumours and skin lesions in a clinical setting.

Clinical and experimental dermatology May 11, 2021
Diagnosis, Differential Skin Diseases Humans Skin Neoplasms Diagnosis, Computer-Assisted Neural Networks, Computer Software
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A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis.

IEEE journal of biomedical and health informatics May 11, 2021
Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of malignant skin tumors is a significant goal, especially considering treatment is normally effective if the tumor is detected early. Limited published hist...
Skin Neoplasms Deep Learning Skin Humans
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Dark corner artefact and diagnostic performance of a market-approved neural network for skin cancer classification.

Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG May 10, 2021
BACKGROUND AND OBJECTIVES: Convolutional neural networks (CNN) have proven dermatologist-level performance in skin lesion classification. Prior to a broader clinical application, an assessment of limitations is crucial. Therefore, the influence of a ...
Skin Neoplasms Deep Learning Artifacts Humans Neural Networks, Computer Prospective Studies
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Domain adaptation and self-supervised learning for surgical margin detection.

International journal of computer assisted radiology and surgery May 6, 2021
PURPOSE: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in s...
Humans Machine Learning Algorithms Margins of Excision Female Mastectomy, Segmental Calibration Operating Rooms Stochastic Processes Breast Mastectomy Supervised Machine Learning Skin Neoplasms Carcinoma, Basal Cell Reproducibility of Results Sensitivity and Specificity Breast Neoplasms Area Under Curve Skin
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Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning.

Computers in biology and medicine Apr 28, 2021
Accurate automated medical image recognition, including classification and segmentation, is one of the most challenging tasks in medical image analysis. Recently, deep learning methods have achieved remarkable success in medical image classification ...
Deep Learning Humans Bayes Theorem Uncertainty Neural Networks, Computer Skin Neoplasms
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Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.

European journal of cancer (Oxford, England : 1990) Apr 7, 2021
BACKGROUND: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.
Germany Nevus Microscopy Female Sex Factors Image Interpretation, Computer-Assisted Melanoma Skin Neoplasms Neural Networks, Computer Databases, Factual Retrospective Studies Male Middle Aged Age Factors Predictive Value of Tests Reproducibility of Results Aged Adult Humans
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Deep-learning approach in the study of skin lesions.

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) Apr 6, 2021
BACKGROUND: Medical technology is far from reaching its full potential. An area that is currently expanding is that of precision medicine. The aim of this article is to present an application of precision medicine-a deep-learning approach to computer...
Skin Neoplasms Diagnosis, Computer-Assisted Deep Learning Skin Diseases Humans
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Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images.

Computer methods and programs in biomedicine Apr 3, 2021
BACKGROUND AND OBJECTIVE: The purpose of the present study was to investigate low-shot deep learning models applied to conjunctival melanoma detection using a small dataset with ocular surface images.
Humans Neural Networks, Computer Melanoma Nevus, Pigmented Skin Neoplasms Deep Learning
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