AIMC Topic: Melanoma
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Pathologist-level classification of histopathological melanoma images with deep neural networks.
European journal of cancer (Oxford, England : 1990)
May 23, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25-26% ...
Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.
Computer methods and programs in biomedicine
May 15, 2019
BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like re...
Efficient learning from big data for cancer risk modeling: A case study with melanoma.
Computers in biology and medicine
Apr 30, 2019
BACKGROUND: Building cancer risk models from real-world data requires overcoming challenges in data preprocessing, efficient representation, and computational performance. We present a case study of a cloud-based approach to learning from de-identifi...
A comparative study of deep learning architectures on melanoma detection.
Tissue & cell
Apr 22, 2019
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instrumen...
Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.
European journal of cancer (Oxford, England : 1990)
Apr 10, 2019
BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For ...
A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.
European journal of cancer (Oxford, England : 1990)
Mar 8, 2019
BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively ...
Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.
European journal of cancer (Oxford, England : 1990)
Feb 22, 2019
BACKGROUND: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comp...
Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.
International journal of computer assisted radiology and surgery
Feb 16, 2019
PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportunities to describe tumor immune cell interactions within the tumor microenvironment and thus provide p...
Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.
International journal of medical informatics
Jan 18, 2019
OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cance...
Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.
Clinical imaging
Sep 11, 2018
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma.
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