https://biometry.nci.nih.gov/cdas/approved-projects/2154/
Lung lesions segmentation and cancer prediction

Principal Investigator

Name: Ha Nguyen
Degrees: Ph.D.
Institution: Vingroup Big Data Institute
Position Title: Research Scientist
Email: nguyenquyha@gmail.com

Project Information

Study: NLST
Project ID: NLST-504
Title: Lung lesions segmentation and cancer prediction

Summary

Lung cancer is the second most popular cancer in Vietnam (a little bit behind liver cancer). The diagnosis and treatment of lung cancer put an extremely high pressure on doctors, especially at the national hospitals. We are therefore in an urgent need for a computer-aided diagnosis (CADx) system that is able to automatically identify the lung lesions and predict lung cancer. This system will not only accelerate the doctors' diagnosis but also serve as a second opinion for doctors' reference. Furthermore, such system can also be used in large-scale screening with low cost.

In this project, we leverage recent advancements in machine learning to detect lung lesions from CT scans and predict lung cancer from both CT scans and pathological images. In particular, deep learning models will be trained on large datasets of lung CT scans to identify and segment the tumors. The cancer likelihood will be then estimated by combining the characteristics of the tumors with their corresponding biopsies. To achieve the final diagnosis, we integrate both image features and other clinical variables (like age, smoking habit and medical history), as what doctors routinely do. We believe that the fusion of heterogeneous data (CT, pathology, clinical) will significantly boost the precision of the automated diagnosis.

Aims

• Improve state-of-the-art deep learning models for lung lesions segmentation
• Improve state-of-the-art algorithms for lung cancer prediction by combining CT scans with pathological images and clinical variables
• Publish the research results in prestigious conferences and journals

Collaborators

Dat Ngo - Vingroup Big Data Institute