AIMC Topic: Dermoscopy
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Characteristics of publicly available skin cancer image datasets: a systematic review.
The Lancet. Digital health
Nov 9, 2021
Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to id...
Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.
PloS one
Sep 22, 2021
Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great...
Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms.
Journal of cancer research and clinical oncology
Sep 21, 2021
PURPOSE: Non-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is confirmed that dermoscopy of NMSC confers a diagnostic advantage as compared to visual face-to-face assessment. COVID-19 restrictions diagnostics by t...
Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
European journal of cancer (Oxford, England : 1990)
Sep 8, 2021
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...
An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification.
IEEE journal of biomedical and health informatics
Sep 3, 2021
Computer-aided skin cancer classification systems built with deep neural networks usually yield predictions based only on images of skin lesions. Despite presenting promising results, it is possible to achieve higher performance by taking into accoun...
AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function.
Scientific reports
Sep 1, 2021
Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent researches have used artificial intelligence to classify melanoma and nevu...
Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.
Microscopy research and technique
Aug 27, 2021
Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effective...
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 ...
Predicting the clinical management of skin lesions using deep learning.
Scientific reports
Apr 8, 2021
Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin conditio...
Comparison of performance of a deep learning-based mobile application with non-dermatologist physicians in the diagnosis of common skin diseases.
International journal of dermatology
Mar 2, 2021
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