# Annotation-efficient, patch-based, explainable deep learning using curriculum method for breast cancer detection in screening mammography

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/annotation-efficient-patch-based-explainable-deep-learning-using-curriculum/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ozden Camurdan,Toygar Tanyel,Esma Çerekçi,Deniz Alış,Emine Meltem,Nurper Denizoğlu,Mustafa Ege Şeker,İlkay Öksüz,Ercan Karaarslan |
| Citations | 5 |
| DOI | 10.1186/s13244-025-01922-w |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-025-01922-w |
| OpenAlex ID | https://openalex.org/W4408644224 |
| PMID | 40106066 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Emine Meltem](https://scholariq.org/researchers/emine-meltem/)

## 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/)
- [Global Cancer Incidence and Screening](https://scholariq.org/topics/global-cancer-incidence-and-screening/)
- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)

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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.