README.md

# OpenMM-Medical

## Introduction

OpenMM-Medical is a comprehensive large-scale medical evaluation dataset that spans multiple domains, including Magnetic Resonance Imaging (MRI), CT scans, X-rays, microscopy images, endoscopy, fundus imaging, and dermoscopy. 
OpenMM-Medical is an integration of existing datasets, comprising a total of 88,996 entries. It is designed to advance the development of multimodal medical large language models within the research community.

Components | Content | Type | Number | Metrics
| :----: | :----: |:----: | :----: |:----: |
ACRIMA | Fundus Photography | Multiple Choice Question Answering | 159 | Acc
Adam Challenge | Endoscopy | Multiple Choice Question Answering | 87 | Acc
ALL Challenge | Microscopy Images | Multiple Choice Question Answering | 342 | Acc
BioMediTech | Microscopy Images | Multiple Choice Question Answering | 511 | Acc
Blood Cell | Microscopy Images | Multiple Choice Question Answering | 1175 | Acc
BreakHis | Magnetic Resonance Imaging | Multiple Choice Question Answering | 735 | Acc
Chest CT Scan | CT Imaging | Multiple Choice Question Answering | 871 | Acc
Chest X-Ray PA | X-Ray | Multiple Choice Question Answering | 850 | Acc
CoronaHack | X-Ray | Multiple Choice Question Answering | 684 | Acc
Covid CT | CT Imaging | Multiple Choice Question Answering | 199 | Acc
Covid-19 tianchi | X-Ray | Multiple Choice Question Answering | 96 | Acc
Covid19 heywhale | X-Ray | Multiple Choice Question Answering | 690 | Acc
COVIDx CXR-4 | X-Ray | Multiple Choice Question Answering | 485 | Acc
CRC100k | Magnetic Resonance Imaging | Multiple Choice Question Answering | 1322 | Acc
DeepDRiD | Fundus Photography | Multiple Choice Question Answering | 131 | Acc
Diabetic Retinopathy | Fundus Photography | Multiple Choice Question Answering | 2051 | Acc
DRIMDB | Fundus Photography | Multiple Choice Question Answering | 132 | Acc
Fitzpatrick 17k | Dermoscopy | Multiple Choice Question Answering | 1552 | Acc
HuSHeM | Microscopy Images | Multiple Choice Question Answering | 89 | Acc
ISBI2016 | Dermoscopy | Multiple Choice Question Answering | 681 | Acc
ISIC2018 | Dermoscopy | Multiple Choice Question Answering | 272 | Acc
ISIC2019 | Dermoscopy | Multiple Choice Question Answering | 1952 | Acc
ISIC2020 | Dermoscopy | Multiple Choice Question Answering | 1580 | Acc
JSIEC | Fundus Photography | Multiple Choice Question Answering | 220 | Acc
Knee Osteoarthritis | X-Ray | Multiple Choice Question Answering | 518 | Acc
MAlig Lymph | Magnetic Resonance Imaging | Multiple Choice Question Answering | 149 | Acc
MHSMA | Microscopy Images | Multiple Choice Question Answering | 1282 | Acc
MIAS | X-Ray | Multiple Choice Question Answering | 142 | Acc
Monkeypox Skin Image 2022 | Dermoscopy | Multiple Choice Question Answering | 163 | Acc
Mura | X-Ray | Multiple Choice Question Answering | 1464 | Acc
NLM- Malaria Data | Magnetic Resonance Imaging | Multiple Choice Question Answering | 75 | Acc
OCT & X-Ray 2017 | X-Ray, Optical Coherence Tomography | Multiple Choice Question Answering | 1301 | Acc
OLIVES | Fundus Photography | Multiple Choice Question Answering | 593 | Acc
PAD-UFES-20 | Dermoscopy | Multiple Choice Question Answering | 479 | Acc
PALM2019 | Fundus Photography | Multiple Choice Question Answering | 510 | Acc
Pulmonary Chest MC | X-Ray | Multiple Choice Question Answering | 38 | Acc
Pulmonary Chest Shenzhen | X-Ray | Multiple Choice Question Answering | 296 | Acc
RadImageNet | CT; Magnetic Resonance Imaging; Ultrasound | Multiple Choice Question Answering | 56697 | Acc
Retinal OCT-C8 | Optical Coherence Tomography | Multiple Choice Question Answering | 4016 | Acc
RUS CHN | X-Ray | Multiple Choice Question Answering | 1982 | Acc
SARS-CoV-2 CT-scan | CT | Multiple Choice Question Answering | 910 | Acc
Yangxi | Fundus Photography | Multiple Choice Question Answering | 1515 | Acc

## Usage

The following steps detail how to use [**Baichuan-Omni-1.5**](https://github.com/baichuan-inc/Baichuan-Omni-1.5) with OpenMM-Medical for evaluation using [**VLMEvalKit**](https://github.com/open-compass/VLMEvalKit):

---

### **1. Add `baichuan.py` in `VLMEvalKit/vlmeval/vlm`**

Download `baichuan.py` (which defines the `Baichuan` model class) and add it in `VLMEvalKit/vlmeval/vlm`.

---

### **2. Modify `VLMEvalKit/vlmeval/vlm/__init__.py`**
Add the following line:
```python
from .baichuan import Baichuan
```

---

### **3. Modify `VLMEvalKit/vlmeval/config.py`**
Import the `Baichuan` model:
```python
from vlmeval.vlm import Baichuan
```

Add the `Baichuan-omni` model configuration:
```python
'Baichuan-omni': partial(
    Baichuan, 
    sft=True, 
    model_path='/your/path/to/the/model/checkpoint'
)
```

---

### **4. Modify `VLMEvalKit/vlmeval/dataset/image_mcq.py`**
Download `image_mcq.py` and add the following code to define the `OpenMMMedical` class. Ensure the `image_folder` points to your OpenMM-Medical dataset location:

```python
class OpenMMMedical(ImageMCQDataset):

    @classmethod
    def supported_datasets(cls):
        return ['OpenMMMedical']

    def load_data(self, dataset='OpenMMMedical'):
        image_folder = "/your/path/to/OpenMM_Medical"
        def generate_tsv(pth):
            import csv
            from pathlib import Path
            tsv_file_path = os.path.join(LMUDataRoot(), f'{dataset}.tsv')
        ...
```

---

### **5. Update `VLMEvalKit/vlmeval/dataset/__init__.py`**
Import `OpenMMMedical`:
```python
from .image_mcq import (
    ImageMCQDataset, MMMUDataset, CustomMCQDataset, 
    MUIRDataset, GMAIMMBenchDataset, MMERealWorld, OpenMMMedical
)

IMAGE_DATASET = [
    ImageCaptionDataset, ImageYORNDataset, ImageMCQDataset, ImageVQADataset,
    MathVision, MMMUDataset, OCRBench, MathVista, LLaVABench, MMVet,
    MTVQADataset, TableVQABench, MMLongBench, VCRDataset, MMDUDataset,
    DUDE, SlideVQA, MUIRDataset, GMAIMMBenchDataset, MMERealWorld, OpenMMMedical
]
```

---

### **6. Update `VLMEvalKit/vlmeval/dataset/image_base.py`**
Modify the `img_root_map` function:
```python
def img_root_map(dataset):
    if 'OpenMMMedical' in dataset:
        return 'OpenMMMedical'
    if 'OCRVQA' in dataset:
        return 'OCRVQA'
    if 'COCO_VAL' == dataset:
        return 'COCO'
    if 'MMMU' in dataset:
        return 'MMMU'
```

---

### **7. Run the Evaluation**
Execute the following command to start the evaluation:
```bash
python run.py --data OpenMMMedical --model Baichuan-omni --verbose
```

---

### **Notes:**
- Ensure that all paths (e.g., `/your/path/to/OpenMM_Medical`) are correctly specified.
- Confirm that the Baichuan model checkpoint is accessible at the defined `model_path`.
- Validate the dependencies and configurations of VLMEvalKit to avoid runtime issues.

With this setup, you should be able to evaluate OpenMM-Medical using Baichuan-Omni successfully.