# MTUNet +  + : explainable few-shot medical image classification with generative adversarial network

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/mtunet-explainable-few-shot-medical-image-classification-with-generative/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ankit Kumar Titoriya,Maheshwari Prasad Singh,Amit Kumar Singh |
| Citations | 7 |
| DOI | 10.1007/s11042-024-19316-3 |
| Fields | Computer Science,Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4396738499 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Ankit Kumar Titoriya](https://scholariq.org/researchers/ankit-kumar-titoriya/)

## 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/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [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.