TahaKoleilat/BiomedCoOp

Introduction

Our study includes 11 biomedical image classification datasets. Place all the datasets in one directory under data to ease management. The file structure looks like

data/
|–– BTMRI/
|–– BUSI/
|–– CHMNIST/
|–– COVID_19/
|–– CTKidney/
|–– DermaMNIST/
|–– KneeXray/
|–– Kvasir/
|–– LungColon/
|–– OCTMNIST/
|–– RETINA/

Datasets Description

Modality	Organ(s)	Name	Classes	# train/val/test
Computerized Tomography	Kidney	CTKidney	Kidney Cyst, Kidney Stone, Kidney Tumor, Normal Kidney	6221/2487/3738
Dermatoscopy	Skin	DermaMNIST	Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanocytic nevus, Melanoma, Vascular Lesion	7007/1003/2005
Endoscopy	Colon	Kvasir	Dyed Lifted Polyps, Normal Cecum, Esophagitis, Dyed Resection Margins, Normal Pylorus, Normal Z Line, Polyps, Ulcerative Colitis	2000/800/1200
Fundus Photography	Retina	RETINA	Cataract, Diabetic Retinopathy, Glaucoma, Normal Retina	2108/841/1268
Histopathology	Lung, Colon	LC25000	Colon Adenocarcinoma, Colon Benign Tissue, Lung Adenocarcinoma, Lung Benign Tissue, Lung Squamous Cell Carcinoma	12500/5000/7500
Histopathology	Colorectal	CHMNIST	Adipose Tissue, Complex Stroma, Debris, Empty Background, Immune Cells, Normal Mucosal Glands, Simple Stroma, Tumor Epithelium	2496/1000/1504
Magnetic Resonance Imaging	Brain	BTMRI	Glioma Tumor, Meningioma Tumor, Normal Brain, Pituitary Tumor	2854/1141/1717
Optical Coherence Tomography	Retina	OCTMNIST	Choroidal Neovascularization, Drusen, Diabetic Macular Edema, Normal	97477/10832/1000
Ultrasound	Breast	BUSI	Benign Tumors, Malignant Tumors, Normal Scans	389/155/236
X-Ray	Chest	COVID-QU-Ex	COVID-19, Lung Opacity, Normal Lungs, Viral Pneumonia	10582/4232/6351
X-Ray	Knee	KneeXray	No, Doubtful, Minimal, Moderate, and Severe Osteoarthritis	5778/826/1656

Download the datasets

All the datasets can be found here on HuggingFace. Download each dataset seperately:

• BTMRI [Drive | HuggingFace]
• BUSI [Drive | HuggingFace]
• CHMNIST [Drive | HuggingFace]
• COVID_19 [Drive | HuggingFace]
• CTKidney [Drive | HuggingFace]
• DermaMNIST [Drive | HuggingFace]
• KneeXray [Drive | HuggingFace]
• Kvasir [Drive | HuggingFace]
• LungColon [Drive | HuggingFace]
• OCTMNIST [Drive | HuggingFace]
• RETINA [Drive | HuggingFace]

After downloading each dataset, unzip and place each under its respective directory like the following

BTMRI/
|–– BTMRI/
|   |–– glioma_tumor/
|   |–– meningioma_tumor/
|   |–– normal_brain/
|   |–– pituitary_tumor/
|–– split_BTMRI.json

Citation

If you use our work, please consider citing:

@article{koleilat2024biomedcoop,
        title={BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models},
        author={Koleilat, Taha and Asgariandehkordi, Hojat and Rivaz, Hassan and Xiao, Yiming},
        journal={arXiv preprint arXiv:2411.15232},
        year={2024}
}