# Automated pulmonary nodule detection using 3D deep convolutional neural networks

arXiv: https://arxiv.org/abs/1903.09876 · HTML: https://mlsanity.com/paper/1903.09876/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation](https://mlsanity.com/paper/2110.10403/) (2021)
- [MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2110.15327/) (2021)
- [A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology](https://mlsanity.com/paper/2503.14933/) (2025)
- [Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2102.00280/) (2021)
- [DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation](https://mlsanity.com/paper/2304.14571/) (2023)
- [AttentNet: Fully Convolutional 3D Attention for Lung Nodule Detection](https://mlsanity.com/paper/2407.14464/) (2024)

## Abstract (arXiv)

Early detection of pulmonary nodules in computed tomography (CT) images is essential for successful outcomes among lung cancer patients. Much attention has been given to deep convolutional neural network (DCNN)-based approaches to this task, but models have relied at least partly on 2D or 2.5D components for inherently 3D data. In this paper, we introduce a novel DCNN approach, consisting of two stages, that is fully three-dimensional end-to-end and utilizes the state-of-the-art in object detection. First, nodule candidates are identified with a U-Net-inspired 3D Faster R-CNN trained using online hard negative mining. Second, false positive reduction is performed by 3D DCNN classifiers trained on difficult examples produced during candidate screening. Finally, we introduce a method to ensemble models from both stages via consensus to give the final predictions. By using this framework, we ranked first of 2887 teams in Season One of Alibaba's 2017 TianChi AI Competition for Healthcare.