mlsanityData to 7 Oct 2026

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

arXiv 1706.04303 ↗First posted 14 Jun 2017cs.CVcited by 26

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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).

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2017.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 5; 2024: 4; 2025: 1.

26 papers.

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  1. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  2. 2021nnDetection: A Self-configuring Method for Medical Object Detectioncited by 60
  3. 2021FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysiscited by 60
  4. 2021AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentationcited by 48
  5. 2021SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbationcited by 19
  6. 2021SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matchingcited by 12
  7. 2021Stabilized Medical Image Attackscited by 10
  8. 2021DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysiscited by 7
  9. 2022Fine-grained Correlation Loss for Regressioncited by 4
  10. 2024Autoregressive Sequence Modeling for 3D Medical Image Representationcited by 4
  11. 2022Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurementscited by 3
  12. 2022LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detectioncited by 3
14 more
  1. 2021AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomicscited by 2
  2. 2022Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learningcited by 2
  3. 2023SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attentioncited by 2
  4. 2021Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT imagescited by 1
  5. 2025A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncologycited by 1
  6. 2021Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scanscited by 0
  7. 2022MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detectioncited by 0
  8. 2023Integrating features from lymph node stations for metastatic lymph node detectioncited by 0
  9. 2023Unsupervised Cross-domain Pulmonary Nodule Detection without Source Datacited by 0
  10. 2023DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentationcited by 0
  11. 2023Gravity Network for end-to-end small lesion detectioncited by 0
  12. 2024A Lung Nodule Dataset with Histopathology-based Cancer Type Annotationcited by 0
  13. 2024AttentNet: Fully Convolutional 3D Attention for Lung Nodule Detectioncited by 0
  14. 2024IAFI-FCOS: Intra- and across-layer feature interaction FCOS model for lesion detection of CT imagescited by 0