# Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/dense-biased-networks-with-deep-priori-anatomy-and-hard-region-adaptation-semi/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yuting He,Guanyu Yang,Jian Yang,Yang Chen,Youyong Kong,Jiasong Wu,Lijun Tang,Xiaomei Zhu,Jean‐Louis Dillenseger,Pengfei Shao,Shaobo Zhang,Huazhong Shu,Jean-Louis Coatrieux,Shuo Li |
| Citations | 79 |
| DOI | 10.1016/j.media.2020.101722 |
| Fields | Computer Science,Engineering,Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3025299467 |
| PMID | 32434127 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Lijun Tang](https://scholariq.org/researchers/lijun-tang/)

## Paper primary topic

- [Renal and Vascular Pathologies](https://scholariq.org/topics/renal-and-vascular-pathologies/)

## Paper topics

- [Renal and Vascular Pathologies](https://scholariq.org/topics/renal-and-vascular-pathologies/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Advanced X-ray and CT Imaging](https://scholariq.org/topics/advanced-x-ray-and-ct-imaging/)

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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.