# Constellation Loss: Improving the Efficiency of Deep Metric Learning Loss Functions for the Optimal Embedding of histopathological images

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/constellation-loss-improving-the-efficiency-of-deep-metric-learning-loss/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Alfonso Medela,Artzai Picón |
| Citations | 24 |
| DOI | 10.4103/jpi.jpi_41_20 |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.sciencedirect.com/science/article/pii/S215335392200267X/pdf |
| OpenAlex ID | https://openalex.org/W3109966562 |
| PMID | 33828896 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Alfonso Medela](https://scholariq.org/researchers/alfonso-medela/)

## 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/)
- [Cell Image Analysis Techniques](https://scholariq.org/topics/cell-image-analysis-techniques/)
- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)

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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.