QuEST Lung Nodule Detection in CT scans

QuEST Lung Nodule Detection in CT scans

A new CAD system which uses fully convolutional DetectNet architecture for real time detection and localization of lung nodules in low dose CT scans. Lung cancer is the leading cause of cancer related deaths in the world. The survival rate can be improved if the presence of lung nodules are detected early. This has also led to more focus being given to computer aided detection (CAD) and diagnosis of lung nodules. The arbitrariness of shape, size and texture of lung nodules is a challenge to be faced when developing these detection systems. In the proposed work we use convolutional neural networks to learn the features for nodule detection, replacing the traditional method of handcrafting features like geometric shape or texture. The model can be deployed in CPUs. This solution plays an important role in reducing the reading time in a cost effective way.

*Please note that member solutions are often customizable to meet the needs of individual enterprise end users.

CONTACT COMPANY

CATEGORIES

China (PRC) India Korea Other - Asia Pacific Other - Europe and Africa Other - North and South America Taiwan Worldwide Intel® Core™ Processor Family Intel® Xeon Scalable Caffe Healthcare Models can be trained - requires labeled data Linux Intel® Distribution for Python Intel® Distribution of OpenVINO™ toolkit Other Medical imaging, analysis and diagnostics Machine Learning