Skin Cancer

Artificial intelligence and immunotherapy.

MHCSeqNet: a deep neural network model for universal MHC binding prediction.

BACKGROUND: Immunotherapy is an emerging approach in cancer treatment that activates the host immune...

Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.

BACKGROUND: The recent success of immunotherapy in treating tumors has attracted increasing interest...

Pathologist-level classification of histopathological melanoma images with deep neural networks.

BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue...

Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides.

BACKGROUND: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising ...

Do no harm: Natural language processing of social media supports safety of aseptic allergen immunotherapy procedures.

Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.

BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Derma...

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 s...

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 ...

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...

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 elem...

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 de...

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 clas...

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

BACKGROUND: Several recent publications have demonstrated the use of convolutional neural networks t...

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 histocompatibili...

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 con...

Attention Residual Learning for Skin Lesion Classification.

Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnos...

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...

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 rem...

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 progr...

Precision immunoprofiling by image analysis and artificial intelligence.

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to in...