AIMC Topic: Dermoscopy

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Computational neural network in melanocytic lesions diagnosis: artificial intelligence to improve diagnosis in dermatology?

European journal of dermatology : EJD
Diagnosis in dermatology is largely based on contextual factors going far beyond the visual and dermoscopic inspection of a lesion. Diagnostic tools such as the different types of dermoscopy, confocal microscopy and optical coherence tomography (OCT)...

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

JAMA dermatology
IMPORTANCE: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

Annals of oncology : official journal of the European Society for Medical Oncology
BACKGROUND: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.

Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment.

A deep bag-of-features model for the classification of melanomas in dermoscopy images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level representations using machine learning techniques. Such interest has been growing steadily in the fi...

Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.

Journal of biomedical optics
Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately represen...

A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition.

Studies in health technology and informatics
Advanced techniques in machine learning combined with scalable "cloud" computing infrastructure are driving the creation of new and innovative health diagnostic applications. We describe a service and application for performing image training and rec...