# Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1706.04303 · HTML: https://mlsanity.com/paper/1706.04303/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

## Built on by (most cited first, 26 of 26)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis](https://mlsanity.com/paper/2112.01148/) (2021)
- [nnDetection: A Self-configuring Method for Medical Object Detection](https://mlsanity.com/paper/2106.00817/) (2021)
- [AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation](https://mlsanity.com/paper/2110.10403/) (2021)
- [SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation](https://mlsanity.com/paper/2106.01544/) (2021)
- [SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching](https://mlsanity.com/paper/2104.05215/) (2021)
- [Stabilized Medical Image Attacks](https://mlsanity.com/paper/2103.05232/) (2021)
- [DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis](https://mlsanity.com/paper/2106.06744/) (2021)
- [Fine-grained Correlation Loss for Regression](https://mlsanity.com/paper/2207.00347/) (2022)
- [Autoregressive Sequence Modeling for 3D Medical Image Representation](https://mlsanity.com/paper/2409.08691/) (2024)
- [Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning](https://mlsanity.com/paper/2203.16606/) (2022)
- [Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements](https://mlsanity.com/paper/2205.13835/) (2022)
- [LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection](https://mlsanity.com/paper/2208.02122/) (2022)
- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention](https://mlsanity.com/paper/2303.03625/) (2023)
- [Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images](https://mlsanity.com/paper/2110.13036/) (2021)
- [A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology](https://mlsanity.com/paper/2503.14933/) (2025)
- [Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans](https://mlsanity.com/paper/2103.14537/) (2021)
- [MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection](https://mlsanity.com/paper/2201.13392/) (2022)
- [Integrating features from lymph node stations for metastatic lymph node detection](https://mlsanity.com/paper/2301.03202/) (2023)
- [Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data](https://mlsanity.com/paper/2304.01085/) (2023)
- [DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation](https://mlsanity.com/paper/2304.14571/) (2023)
- [Gravity Network for end-to-end small lesion detection](https://mlsanity.com/paper/2309.12876/) (2023)
- [A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation](https://mlsanity.com/paper/2406.18102/) (2024)
- [AttentNet: Fully Convolutional 3D Attention for Lung Nodule Detection](https://mlsanity.com/paper/2407.14464/) (2024)
- [IAFI-FCOS: Intra- and across-layer feature interaction FCOS model for lesion detection of CT images](https://mlsanity.com/paper/2409.00694/) (2024)

## Abstract (arXiv)

Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).