An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction
This addresses the inefficiency and sub-optimal performance of existing two-step deep learning systems for lung disease screening, offering a more resource-efficient solution for medical imaging.
The paper tackles the problem of pulmonary nodule detection in CT scans by integrating nodule candidate screening and false positive reduction into a single end-to-end model, improving performance by 3.88% over the two-step approach while reducing model complexity and inference time.
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.