# Towards automatic pulmonary nodule management in lung cancer screening with deep learning

arXiv: https://arxiv.org/abs/1610.09157 · HTML: https://mlsanity.com/paper/1610.09157/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2016.

## Built on by (most cited first, 11 of 11)

- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)
- [Deep Learning Applications for Lung Cancer Diagnosis: A systematic review](https://mlsanity.com/paper/2201.00227/) (2022)
- [Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging](https://mlsanity.com/paper/2307.16694/) (2023)
- [An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth](https://mlsanity.com/paper/2104.08789/) (2021)
- [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)
- [Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data](https://mlsanity.com/paper/2108.00250/) (2021)
- [Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark](https://mlsanity.com/paper/2201.00458/) (2022)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network](https://mlsanity.com/paper/2208.07855/) (2022)
- [A Machine Learning Approach to Volumetric Computations of Solid Pulmonary Nodules](https://mlsanity.com/paper/2508.20127/) (2025)

## Abstract (arXiv)

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.