AIMC Topic: Dermoscopy

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Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features.

IEEE transactions on bio-medical engineering
In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very de...

Skin lesion classification with ensembles of deep convolutional neural networks.

Journal of biomedical informatics
Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9000 deaths in the United States each year. Thus, reliable automatic mel...

Deep Tissue Sequencing Using Hypodermoscopy and Augmented Intelligence to Analyze Atypical Pigmented Lesions.

Journal of cutaneous medicine and surgery
BACKGROUND: Over the past decade, new technologies, devices, and methods have been developed to assist in the diagnosis of cutaneous melanocytic lesions.

Dense Deconvolutional Network for Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics
Automatic delineation of skin lesion contours from dermoscopy images is a basic step in the process of diagnosis and treatment of skin lesions. However, it is a challenging task due to the high variation of appearances and sizes of skin lesions. In o...

Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features.

IEEE journal of biomedical and health informatics
The presence of certain clinical dermoscopic features within a skin lesion may indicate melanoma, and automatically detecting these features may lead to more quantitative and reproducible diagnoses. We reformulate the task of classifying clinical der...

Acral melanoma detection using a convolutional neural network for dermoscopy images.

PloS one
BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the h...

DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis.

IEEE journal of biomedical and health informatics
Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutio...

Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks.

IEEE journal of biomedical and health informatics
Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is ...

Recognition of pigment network pattern in dermoscopy images based on fuzzy classification of pixels.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: One of the most relevant dermoscopic patterns is the pigment network. An innovative method of pattern recognition is presented for its detection in dermoscopy images.