# X-ray Dissectography Improves Lung Nodule Detection

arXiv: https://arxiv.org/abs/2203.13118 · HTML: https://mlsanity.com/paper/2203.13118/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction](https://mlsanity.com/paper/2105.11692/) (2021)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection](https://mlsanity.com/paper/2205.00122/) (2022)

## Abstract (arXiv)

Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules difficult to detect. In this study, we apply "X-ray dissectography" to dissect lungs digitally from a few radiographic projections, suppress the interference of irrelevant structures, and improve lung nodule detectability. For this purpose, a collaborative detection network is designed to localize lung nodules in 2D dissected projections and 3D physical space. Our experimental results show that our approach can significantly improve the average precision by 20+% in comparison with the common baseline that detects lung nodules from original projections using a popular detection network. Potentially, this approach could help re-design the current X-ray imaging protocols and workflows and improve the diagnostic performance of chest radiographs in lung diseases.