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---
license: apache-2.0
datasets:
- ibrahimhamamci/CT-RATE
metrics:
- bleu
- bertscore
- rouge
base_model:
- microsoft/Phi-3-mini-4k-instruct
tags:
- biology
- medical
---
# Welcome to SAMF model [MICCAI' 25]!
**[MICCAI' 25] From Slices to Volumes: Multi-Scale Fusion of 2D and 3D Features for CT Scan Report Generation**
| **Model** | **Bleu1** | **Bleu4** | **RougeL** | **Meteor** | **Bert F1** | **Llama Score** |
|-----------------------|-----------|-----------|------------|------------|-------------|-----------------|
| CT2Rep | 0.309 | 0.172 | 0.243 | 0.173 | 0.865 | 6.35 |
| CT-Chat | 0.395 | - | 0.321 | 0.219 | - | 5.664 |
| Our Baseline (SAMF) | 0.423 | 0.203 | 0.338 | 0.356 | 0.879 | 6.792 |
| SAMF + *Ao2D* | **0.440** | **0.261** | **0.417** | **0.417** | **0.889** | **7.165** |
## Introduction
*Slice Attentive Multimodal Fusion (SAMF)* , a framework that combines the rich, high-resolution information from 2D slices with the spatial coherence of 3D volumetric data. Experimental results demonstrate that our method outperforms existing baseline approaches in both report generation and multiple-choice question answering, highlighting the critical role of multidimensional feature integration.
## Model Description
- **Model type:** 3D Medical Report Generation and Visual Question Answering
- **Language(s) (NLP):** English
- **License:** apache-2.0
- **Finetuned from model:** microsoft/Phi-3-mini-4k-instruct
### Training Data
- **Medical Report Generation and Visual Question Answering:** [ibrahimhamamci/CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE), default subset
### Hardware Utilization
- **Hardware Type:** 1 × NVIDIA-A100
- **Hours used** around 16 hours
### Evaluation
To perform evaluation using this model, please refer to our GitHub repository ([serag-ai/SAMF](https://github.com/serag-ai/SAMF.git)), which provides detailed information on how to use it. |