Skin Cancer

A hierarchical three-step superpixels and deep learning framework for skin lesion classification.

Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma is one of the most dangerous skin cancer types has a high mortality rate. An estimated 196,060 melanoma cases will be diagnosed in 2020 in the USA. Many computerized techniques are presented in the past to diagnose skin lesions, but they are still failing to achieve significant accuracy. To improve...

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties.

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunotherapeutic drugs. In this work, we evaluated fifteen machine learning algorithms for the classification of tumor antigens. For this purpose, we build r...

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model.

BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a ...

Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of de...

Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition.

BACKGROUND: Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, y...

MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism.

BACKGROUND: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within p...

Melanoma diagnosis using deep learning techniques on dermatoscopic images.

BACKGROUND: Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated wi...

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.

This study aimed to use computed tomography (CT) images to assess PD-L1 expression in non-small cell lung cancer (NSCLC) and predict response to immu...

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a sm...

CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy.

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for ...

Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.

Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative ...

Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma.

The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinoma...

An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design.

Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, ...

A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortalit...

A Deep Learning Approach to Photoacoustic Wavefront Localization in Deep-Tissue Medium.

Optical photons undergo strong scattering when propagating beyond 1-mm deep inside biological tissue. Finding the origin of these diffused optical wav...

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell ca...

Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers.

Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagn...

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

PURPOSE: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pi...

Use of an Artificial Neural Network to Identify Patient Clusters in a Large Cohort of Patients with Melanoma by Simultaneous Analysis of Costs and Clinical Characteristics.

Artificial intelligence for melanoma diagnosis.

Convolutional neural networks (CNN) have shown unprecedented accuracy in digital image analysis, which can be harnessed for melanoma recognition throu...