# An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction

## TL;DR

This paper introduces an end-to-end deep learning framework for pulmonary nodule detection in CT scans that combines candidate screening and false positive reduction, improving accuracy and efficiency over traditional two-step methods.

## Contribution

The work presents a novel integrated model trained jointly for nodule detection and false positive reduction, reducing resource use and enhancing performance.

## Key findings

- Improves detection accuracy by 3.88% over two-step methods.
- Reduces model complexity by one third.
- Cuts inference time by 3.6 times.

## Abstract

Pulmonary nodule detection using low-dose Computed Tomography (CT) is often the first step in lung disease screening and diagnosis. Recently, algorithms based on deep convolutional neural nets have shown great promise for automated nodule detection. Most of the existing deep learning nodule detection systems are constructed in two steps: a) nodule candidates screening and b) false positive reduction, using two different models trained separately. Although it is commonly adopted, the two-step approach not only imposes significant resource overhead on training two independent deep learning models, but also is sub-optimal because it prevents cross-talk between the two. In this work, we present an end-to-end framework for nodule detection, integrating nodule candidate screening and false positive reduction into one model, trained jointly. We demonstrate that the end-to-end system improves the performance by 3.88\% over the two-step approach, while at the same time reducing model complexity by one third and cutting inference time by 3.6 fold. Code will be made publicly available.