# Deep convolutional neural networks for automatic segmentation of thoracic organs‐at‐risk in radiation oncology – use of non‐domain transfer learning

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-convolutional-neural-networks-for-automatic-segmentation-of-thoracic-organs/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Charles C. Vu,Z.A. Siddiqui,Leonid Zamdborg,A. Thompson,Thomas J. Quinn,Edward Castillo,Thomas Guerrero |
| Citations | 23 |
| DOI | 10.1002/acm2.12871 |
| Fields | Engineering,Medicine,Physics and Astronomy |
| Open Access | true |
| OA Status | gold |
| OA URL | https://aapm.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/acm2.12871 |
| OpenAlex ID | https://openalex.org/W3040140780 |
| PMID | 32602187 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Thomas J. Quinn](https://scholariq.org/researchers/thomas-j-quinn/)

## Paper primary topic

- [Advanced Radiotherapy Techniques](https://scholariq.org/topics/advanced-radiotherapy-techniques/)

## Paper topics

- [Advanced Radiotherapy Techniques](https://scholariq.org/topics/advanced-radiotherapy-techniques/)
- [Lung Cancer Diagnosis and Treatment](https://scholariq.org/topics/lung-cancer-diagnosis-and-treatment/)
- [Medical Imaging and Analysis](https://scholariq.org/topics/medical-imaging-and-analysis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.