The integration of convolutional neural networks into dermatology practices, especially those with fewer than 5 years of experience, may increase diagnostic accuracy of skin cancer.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural ...

Through the use of convolutional neural networks accelerated by Nvidia Tesla GPU accelerators, the images produced from a CT scan can be analyzed within minutes.

Researchers have shown for the first time that a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) is better than experienced ...

Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network–Based Artificial Intelligence Tool The best performing model, a Bi-LSTM NER ...

More information: Junming Jian et al, Multiple instance convolutional neural network with modality-based attention and contextual multi-instance learning pooling layer for effective ...

Melanoma probability scores increased due to standard skin markings from surgical ink and were associated with a significant reduction in the specificity of a convolutional neural network trained ...