# Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/automated-quantitative-analysis-of-ki-67-staining-and-he-images-recognition-and/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Min Feng,Yang Deng,Libo Yang,Qiuyang Jing,Zhang Zhang,Lian Xu,Xiaoxia Wei,Yanyan Zhou,Diwei Wu,Xiang Fei,Yizhe Wang,Ji Bao,Hong Bu |
| Citations | 52 |
| DOI | 10.1186/s13000-020-00957-5 |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science,Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://diagnosticpathology.biomedcentral.com/track/pdf/10.1186/s13000-020-00957-5 |
| OpenAlex ID | https://openalex.org/W3031496916 |
| PMID | 32471471 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Zhang Zhang](https://scholariq.org/researchers/zhang-zhang/)

## Paper primary topic

- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)

## Paper topics

- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Breast Cancer Treatment Studies](https://scholariq.org/topics/breast-cancer-treatment-studies/)
- [Breast Lesions and Carcinomas](https://scholariq.org/topics/breast-lesions-and-carcinomas/)

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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.