# A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans

## TL;DR

This paper presents a 3D deep learning system for lung cancer detection and diagnosis from low-dose CT scans, achieving state-of-the-art results and providing calibrated probabilities for better clinical decision-making.

## Contribution

It introduces an end-to-end 3D CNN system that couples detection and diagnosis, incorporates model uncertainty, and improves robustness without false positive reduction stages.

## Key findings

- State-of-the-art detection and classification performance on LUNA16 and Kaggle datasets.
- Coupled detection and diagnosis improves robustness.
- Model uncertainty enables well-calibrated probabilities for clinical use.

## Abstract

We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiting this coupling allows us to develop an end-to-end system that has higher and more robust performance and eliminates the need for a nodule detection false positive reduction stage. Furthermore, we characterize model uncertainty in our deep learning systems, a first for lung CT analysis, and show that we can use this to provide well-calibrated classification probabilities for both nodule detection and patient malignancy diagnosis. These calibrated probabilities informed by model uncertainty can be used for subsequent risk-based decision making towards diagnostic interventions or disease treatments, as we demonstrate using a probability-based patient referral strategy to further improve our results.