# LNDb: A Lung Nodule Database on Computed Tomography

arXiv: https://arxiv.org/abs/1911.08434 · HTML: https://mlsanity.com/paper/1911.08434/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 28 of 28)

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- [Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics](https://mlsanity.com/paper/2111.13069/) (2021)
- [Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation](https://mlsanity.com/paper/2307.15645/) (2023)
- [Evaluation of importance estimators in deep learning classifiers for Computed Tomography](https://mlsanity.com/paper/2209.15398/) (2022)
- [BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors](https://mlsanity.com/paper/2304.08486/) (2023)
- [Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision](https://mlsanity.com/paper/2307.05804/) (2023)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans](https://mlsanity.com/paper/2206.03049/) (2022)
- [Semantic-Topological Graph Reasoning for Language-Guided Pulmonary Screening](https://mlsanity.com/paper/2604.05620/) (2026)
- [iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models](https://mlsanity.com/paper/2605.05761/) (2026)
- [Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation](https://mlsanity.com/paper/2608.14766/) (2026)
- [Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions](https://mlsanity.com/paper/2208.10694/) (2022)
- [SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2411.14525/) (2024)
- [MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation](https://mlsanity.com/paper/2511.13135/) (2025)
- [Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling](https://mlsanity.com/paper/2604.07329/) (2026)
- [Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations](https://mlsanity.com/paper/2606.28391/) (2026)
- [When Does Synthetic CT Transfer? A Label-Free Donor/Host Diagnostic for Medical Vision-Language Model Routing on Real Lung CT](https://mlsanity.com/paper/2606.29232/) (2026)

## Abstract (arXiv)

Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly, time-consuming and prone to variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to clinical routine is challenging. In this study, a new database for the development and testing of pulmonary nodule computer-aided strategies is presented which intends to complement current databases by giving additional focus to radiologist variability and local clinical reality. State-of-the-art nodule detection, segmentation and characterization methods are tested and compared to manual annotations as well as collaborative strategies combining multiple radiologists and radiologists and computer-aided systems. It is shown that state-of-the-art methodologies can determine a patient's follow-up recommendation as accurately as a radiologist, though the nodule detection method used shows decreased performance in this database.