Computer-aided diagnosis system for lung-related pathology
Principal Investigator
Name
Aurélio Campilho
Degrees
Ph.D.
Institution
INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência
Position Title
Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-375
Initial CDAS Request Approval
Nov 15, 2017
Title
Computer-aided diagnosis system for lung-related pathology
Summary
The fields of computer vision and machine learning have contributed, for many years, to the development of diagnosis-aiding and second opinion systems for medical practice. One of the major issues, however, is that highly complexity of medical tasks, such as lung cancer screening, inhibits the application of most of these methods in real clinical practice.
The advances on computational power over the last few years have enabled the usage of high performing methods, such as deep learning, and thus increased the possibility of the development of reliable tools for medical practice. Since these methods require high amounts of data to develop, the NLST images and metadata may prove essential to achieve this goal.
The project aims at the development of machine-learning approaches that provide a second-opinion to clinicians during the diagnosis of lung-related pathologies, namely lung cancer. The NLST data will be used for the development and partial validation of the system.
The advances on computational power over the last few years have enabled the usage of high performing methods, such as deep learning, and thus increased the possibility of the development of reliable tools for medical practice. Since these methods require high amounts of data to develop, the NLST images and metadata may prove essential to achieve this goal.
The project aims at the development of machine-learning approaches that provide a second-opinion to clinicians during the diagnosis of lung-related pathologies, namely lung cancer. The NLST data will be used for the development and partial validation of the system.
Aims
Aims
1) Development of a system capable of detecting abnormalities on lung tissue, with special focus on the detection of lung nodules in CT images;
2) Development of a system that, given a set of abnormalities, properly predicts necessary patient follow-up;
Collaborators
All members of the CBER-Bioimaging Lab research group (www.bioimglab.inesctec.pt), namely: António Cunha, PhD; Guilherme Aresta, PhD student