Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
This work addresses lung cancer diagnosis for medical imaging, but it is incremental as it applies existing deep learning methods to a specific competition dataset.
The paper tackled lung cancer detection from 3D CAT scans using a deep learning framework that detects nodules, assesses malignancy, and assigns cancer probability, achieving 41st place out of 1972 teams in the Kaggle Data Science Bowl 2017.
We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.