Skin Cancer

Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.

BACKGROUND: Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional sonification (data to sound waves conversion) layer on DL algorithms. ...

Efficient learning from big data for cancer risk modeling: A case study with melanoma.

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-identified electronic health record data and demonstrate its effectiveness for melanoma risk prediction.

A comparative study of deep learning architectures on melanoma detection.

Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbi...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunother...

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.

BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious les...

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable...

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.

BACKGROUND: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-ce...

ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm.

The major histocompatibility complex (MHC) is a term for all gene groups of a major histocompatibility antigen. It binds to peptide chains derived fro...

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.

PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportuni...

Attention Residual Learning for Skin Lesion Classification.

Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Althou...

Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.

OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible thr...

Embedding of Genes Using Cancer Gene Expression Data: Biological Relevance and Potential Application on Biomarker Discovery.

Artificial neural networks (ANNs) have been utilized for classification and prediction task with remarkable accuracy. However, its implications for un...

Initial results of pulmonary resection after neoadjuvant nivolumab in patients with resectable non-small cell lung cancer.

OBJECTIVE: We conducted a phase I trial of neoadjuvant nivolumab, a monoclonal antibody to the programmed cell death protein 1 checkpoint receptor, in...

Precision immunoprofiling by image analysis and artificial intelligence.

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This re...

Multimodal skin lesion classification using deep learning.

While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies have generally considered o...

Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.

PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software ...

Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features.

In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy....

Skin lesion classification with ensembles of deep convolutional neural networks.

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 c...

Machine learning model in melanoma.

Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features.

The presence of certain clinical dermoscopic features within a skin lesion may indicate melanoma, and automatically detecting these features may lead ...