mlsanityData to 7 Oct 2026

End-to-end lung nodule detection framework with model-based feature projection block

arXiv 2106.05741 ↗First posted 10 Jun 2021eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

This paper proposes novel end-to-end framework for detecting suspicious pulmonary nodules in chest CT scans. The method core idea is a new nodule segmentation architecture with a model-based feature projection block on three-dimensional convolutions. This block acts as a preliminary feature extractor for a two-dimensional U-Net-like convolutional network. Using the proposed approach along with an axial, coronal, and sagittal projection analysis makes it possible to abandon the widely used false positives reduction step. The proposed method achieves SOTA on LUNA2016 with 0.959 average sensitivity, and 0.936 sensitivity if the false-positive level per scan is 0.25. The paper describes the proposed approach and represents the experimental results on LUNA2016 as well as ablation studies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 10 Jun 2021.

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15 of 32 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
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  5. 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 94
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  7. 2019NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentationcited by 21
  8. 2020SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentationcited by 19
  9. 2019DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detectioncited by 15
  10. 2019Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projectioncited by 10
  11. 2018Towards radiologist-level cancer risk assessment in CT lung screening using deep learningcited by 5
  12. 2019An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reductioncited by 5
3 more
  1. 2018S4ND: Single-Shot Single-Scale Lung Nodule Detectioncited by 4
  2. 2020Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scanscited by 2
  3. 2019Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detectioncited by 1

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