AIMC Topic: Skin Neoplasms
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DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.
BMC bioinformatics
Mar 11, 2020
BACKGROUND: Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers' instances. The survival rates of melanoma from early to terminal stages is mo...
Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy.
Journal of the European Academy of Dermatology and Venereology : JEADV
Mar 4, 2020
BACKGROUND: Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10-20% of the tumours affecting the head and neck area. Radiotherapy is suggested for localized cutaneous squamous cell carcinomas (cSCC) wit...
Towards Interpretable Skin Lesion Classification with Deep Learning Models.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Skin disease is a prevalent condition all over the world. Computer vision-based technology for automatic skin lesion classification holds great promise as an effective screening tool for early diagnosis. In this paper, we propose an accurate and inte...
An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis.
IEEE journal of biomedical and health informatics
Feb 13, 2020
Automatic skin lesion analysis of dermoscopy images remains a challenging topic. In this paper, we propose an end-to-end multi-task deep learning framework for automatic skin lesion analysis. The proposed framework can perform skin lesion detection, ...
Reply to the letter to the editor: 'Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images'.
European journal of cancer (Oxford, England : 1990)
Jan 30, 2020
Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.
Journal of the European Academy of Dermatology and Venereology : JEADV
Jan 21, 2020
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...
Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.
IEEE journal of biomedical and health informatics
Dec 23, 2019
Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligen...
A Machine-Learning Approach to Identify a Prognostic Cytokine Signature That Is Associated With Nivolumab Clearance in Patients With Advanced Melanoma.
Clinical pharmacology and therapeutics
Dec 19, 2019
Lower clearance of immune checkpoint inhibitors is a predictor of improved overall survival (OS) in patients with advanced cancer. We investigated a novel approach using machine learning to identify a baseline composite cytokine signature via clearan...
Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.
Journal of cutaneous pathology
Nov 27, 2019
PURPOSE: Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most difficult areas in dermatopathology. The goal of this study was to identify proteomic differences, which ...
The impact of patient clinical information on automated skin cancer detection.
Computers in biology and medicine
Nov 18, 2019
Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not ...
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