# How Segment Anything Model (Sam) Boost Medical Image Segmentation: A Survey

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/how-segment-anything-model-sam-boost-medical-image-segmentation-a-survey/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yichi Zhang,Rushi Jiao |
| Citations | 235 |
| DOI | 10.2139/ssrn.4495221 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://doi.org/10.2139/ssrn.4495221 |
| OpenAlex ID | https://openalex.org/W4382567565 |
| Type | preprint |
| Year | 2023 |

## Paper authors

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

## Paper journal

- [SSRN Electronic Journal](https://scholariq.org/journals/ssrn-electronic-journal/)

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