# Explainable hybrid vision transformers and convolutional network for multimodal glioma segmentation in brain MRI

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/explainable-hybrid-vision-transformers-and-convolutional-network-for-multimodal/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ramy A. Zeineldin,Mohamed Esmail Karar,Ziad Elshaer,Jan Coburger,Christian Rainer Wirtz,Oliver Burgert,Franziska Mathis-Ullrich |
| Citations | 66 |
| DOI | 10.1038/s41598-024-54186-7 |
| Fields | Computer Science,Medicine,Neuroscience |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.nature.com/articles/s41598-024-54186-7.pdf |
| OpenAlex ID | https://openalex.org/W4391814741 |
| PMID | 38355678 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Franziska Mathis-Ullrich](https://scholariq.org/researchers/franziska-mathis-ullrich/)

## Paper journal

- [Scientific Reports](https://scholariq.org/journals/scientific-reports/)

## Paper primary topic

- [Brain Tumor Detection and Classification](https://scholariq.org/topics/brain-tumor-detection-and-classification/)

## Paper topics

- [Brain Tumor Detection and Classification](https://scholariq.org/topics/brain-tumor-detection-and-classification/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)

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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.