Lung cancer risk estimation on thorax CT scans - DSB2017 grt123


Logo for Lung cancer risk estimation on thorax CT scans - DSB2017 grt123

About

Version:
ffdd0ac4-315e-4879-84ce-8cda68e03989
Last updated:
April 13, 2021, 7:17 a.m.
Associated publications:
Jacobs C, Setio AAA, Scholten ET, et al.. Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists. Radiology: Artificial Intelligence. 2021;3(6).
Liao F, Liang M, Li Z, Hu X, Song S. Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network. IEEE Trans Neural Netw Learning Syst. 2019;30(11):3484-3495.
Inputs:
  • Generic Medical Image 
Outputs:
  • Generic Overlay  (An overlay of unknown type. Legacy, please use alternative interfaces.)
  • Results JSON File  (A collection of results of unknown type. Legacy, if possible please use alternative interfaces.)

Summary

This algorithm analyzes non-contrast CT scans of the thorax and predicts the lung cancer risk. The algorithm is described in this publication by Fangzhou Liao, Ming Liang, Zhe Li, and Xiaolin Hu and the code is publicly available on Github.

This algorithm was developed as part of the Kaggle Data Science Bowl in 2017 and won the first place in this challenge.

Image courtesy of Fangzhou Liao et al. in the previously mentioned paper.

Uses and Directions

This algorithm was developed for research purposes only.