This paper is based on the Tianchi Healthcare AI Competition, an online challenge for automatically detecting lung nodules from computed tomography (CT) scans, cosponsored by Alibaba Cloud, Intel, and LinkDoc. In October 2017, this competition successfully concluded after an intense seven-month competition among 2,887 teams across the globe.
Cancer is a leading cause of death and affects millions of lives every year. Its early detection could help to increase the survival of many lives1 in addition to saving billions of dollars.
Doctor Hazel, a skin cancer screening service powered by artificial intelligence (AI) that operates in real time, relies on an extensive library of images to distinguish between skin cancer and benign lesions, making it easier for people to seek professional medical advice.
Follow an Intel® Student Ambassador who uses machine learning and AI to revolutionize medical diagnostics. These diagnostic techniques used classify the characteristics of epilepsy and Alzheimer's by analyzing how the brain is connected.
Fueling the Next Great Wave of Data-Driven Innovation in the Life Sciences
This article, Machine Learning and Mammography, shows how existing deep learning technologies can be utilized to train artificial intelligence (AI) to be able to detect invasive ductal carcinoma (IDC)1 (breast cancer) in unlabeled histology images.
The aim of the work was to implement, train and evaluate the quality of automated brain tumor multi-label segmentation technique for Magnetic Resonance Imaging based on Tiramisu deep learning architecture.
Building a Black Box Model Using Transfer Learning Introduction
Why We Built Doctor Hazel