Skin Cancer

Radiomics can predict tumour response in patients treated with Nivolumab for a metastatic renal cell carcinoma: an artificial intelligence concept.

Melanoma detection using adversarial training and deep transfer learning.

Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnorma...

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images.

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis ...

Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet.

BACKGROUND: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, earl...

Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study.

Metastasis development represents an important threat for melanoma patients, even when diagnosed at ...

Gene Expression Profile Prediction in Uveal Melanoma Using Deep Learning: A Pilot Study for the Development of an Alternative Survival Prediction Tool.

An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine.

Skin is the outermost and largest organ of the human body that protects us from the external agents....

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.

Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregula...

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell mot...

Radiomics and deep learning in lung cancer.

Lung malignancies have been extensively characterized through radiomics and deep learning. By provid...

Technological advances for the detection of melanoma: Advances in diagnostic techniques.

Managing the balance between accurately identifying early stage melanomas while avoiding obtaining b...

iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation.

In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of can...

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation.

In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin...

What is AI? Applications of artificial intelligence to dermatology.

In the past, the skills required to make an accurate dermatological diagnosis have required exposure...

Deep Learning-Based Methods for Automatic Diagnosis of Skin Lesions.

The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions us...

Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

PURPOSE: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell ...

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.

Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of les...

DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.

BACKGROUND: Melanoma results in the vast majority of skin cancer deaths during the last decades, eve...

A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks.

Many drugs are developed for commonly occurring, well studied cancer drivers such as vemurafenib for...

Artificial intelligence as the next step towards precision pathology.

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of c...

An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis.

Automatic skin lesion analysis of dermoscopy images remains a challenging topic. In this paper, we p...