# Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/hierarchical-combinatorial-deep-learning-architecture-for-pancreas-segmentation/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Min Fu,Wenming Wu,Xiafei Hong,Qiuhua Liu,Jialin Jiang,Yaobin Ou,Yupei Zhao,Xinqi Gong |
| Citations | 76 |
| DOI | 10.1186/s12918-018-0572-z |
| Fields | Computer Science,Medicine,Neuroscience |
| Open Access | true |
| OA Status | gold |
| OA URL | https://bmcsystbiol.biomedcentral.com/counter/pdf/10.1186/s12918-018-0572-z |
| OpenAlex ID | https://openalex.org/W2798997960 |
| PMID | 29745840 |
| Type | article |
| Year | 2018 |

## Paper authors

- [Qiuhua Liu](https://scholariq.org/researchers/qiuhua-liu/)

## Paper primary topic

- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

## Paper topics

- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Pancreatic and Hepatic Oncology Research](https://scholariq.org/topics/pancreatic-and-hepatic-oncology-research/)
- [Brain Tumor Detection and Classification](https://scholariq.org/topics/brain-tumor-detection-and-classification/)

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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.