# Multimodal combined model integrating 2.5D deep learning and habitat radiomics for malignancy discrimination in ≤2 cm BI-RADS 4 breast lesions

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/multimodal-combined-model-integrating-2-5d-deep-learning-and-habitat-radiomics/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Shiyan Guo,Xiaohui Zhou,Jinguang Zhou,Liu Gong,Sijing Zhou,Liqing Jiang,Yan Zhang,Linyuan Jin,Ping Zhou |
| Citations | 0 |
| DOI | 10.21037/qims-2025-1-2812 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | diamond |
| OA URL | https://doi.org/10.21037/qims-2025-1-2812 |
| OpenAlex ID | https://openalex.org/W7155086202 |
| PMID | 42147917 |
| Type | article |
| Year | 2026 |

## Paper authors

- [Jinguang Zhou](https://scholariq.org/researchers/jinguang-zhou/)

## Paper primary topic

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

## Paper topics

- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [MRI in cancer diagnosis](https://scholariq.org/topics/mri-in-cancer-diagnosis/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)

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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.