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

arXiv: https://arxiv.org/abs/2106.05741 · HTML: https://mlsanity.com/paper/2106.05741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation](https://mlsanity.com/paper/1907.11320/) (2019)
- [SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation](https://mlsanity.com/paper/2001.07645/) (2020)
- [DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection](https://mlsanity.com/paper/1904.03501/) (2019)
- [Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection](https://mlsanity.com/paper/1904.05956/) (2019)
- [Towards radiologist-level cancer risk assessment in CT lung screening using deep learning](https://mlsanity.com/paper/1804.01901/) (2018)
- [An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction](https://mlsanity.com/paper/1903.09880/) (2019)
- [S4ND: Single-Shot Single-Scale Lung Nodule Detection](https://mlsanity.com/paper/1805.02279/) (2018)
- [Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans](https://mlsanity.com/paper/2005.03654/) (2020)
- [Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection](https://mlsanity.com/paper/1905.03445/) (2019)

## 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.