# Crowdsourcing Lung Nodules Detection and Annotation

arXiv: https://arxiv.org/abs/1809.06402 · HTML: https://mlsanity.com/paper/1809.06402/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Augmenting Chest X-ray Datasets with Non-Expert Annotations](https://mlsanity.com/paper/2309.02244/) (2023)
- [Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users](https://mlsanity.com/paper/2501.07196/) (2025)

## Abstract (arXiv)

We present crowdsourcing as an additional modality to aid radiologists in the diagnosis of lung cancer from clinical chest computed tomography (CT) scans. More specifically, a complete workflow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd. We combine the concept of overlapping thin-slab maximum intensity projections (TS-MIPs) and cine viewing to render short videos that can be outsourced as an annotation task to the crowd. These videos are generated by linearly interpolating overlapping TS-MIPs of CT slices through the depth of each quadrant of a patient's lung. The resultant videos are outsourced to an online community of non-expert users who, after a brief tutorial, annotate suspected nodules in these video segments. Using our crowdsourcing workflow, we achieved a lung nodule detection sensitivity of over 90% for 20 patient CT datasets (containing 178 lung nodules with sizes between 1-30mm), and only 47 false positives from a total of 1021 annotations on nodules of all sizes (96% sensitivity for nodules>4mm). These results show that crowdsourcing can be a robust and scalable modality to aid radiologists in screening for lung cancer, directly or in combination with computer-aided detection (CAD) algorithms. For CAD algorithms, the presented workflow can provide highly accurate training data to overcome the high false-positive rate (per scan) problem. We also provide, for the first time, analysis on nodule size and position which can help improve CAD algorithms.