Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
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Abstract (arXiv)
We address the problem of supporting radiologists in the longitudinal management of lung cancer. Therefore, we proposed a deep learning pipeline, composed of four stages that completely automatized from the detection of nodules to the classification of cancer, through the detection of growth in the nodules. In addition, the pipeline integrated a novel approach for nodule growth detection, which relied on a recent hierarchical probabilistic U-Net adapted to report uncertainty estimates. Also, a second novel method was introduced for lung cancer nodule classification, integrating into a two stream 3D-CNN network the estimated nodule malignancy probabilities derived from a pretrained nodule malignancy network. The pipeline was evaluated in a longitudinal cohort and reported comparable performances to the state of art.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 62 references that are in the map. First posted on arXiv 26 Mar 2021.
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References · 27
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
- 2018Image Transformercited by 770
- 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 577
- 2019Attention Augmented Convolutional Networkscited by 363
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- 2018nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentationcited by 291
- 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
- 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
- 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 198
- 2019A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguitiescited by 65
- 2017Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Networkcited by 53
- 2018DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classificationcited by 36
- 2017Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networkscited by 26
- 2018Highly accurate model for prediction of lung nodule malignancy with CT scanscited by 14
- 2016Towards automatic pulmonary nodule management in lung cancer screening with deep learningcited by 11
- 2017Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challengecited by 11
- 2019Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networkscited by 7
- 2019Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipelinecited by 5
- 2018Diagnostic Classification Of Lung Nodules Using 3D Neural Networkscited by 4
- 2020Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networkscited by 1
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