TahaKoleilat/BiomedCoOp

Biomedical Few-shot Image Classification for Vision-Language Models

Overview

Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated substantial success in self-supervised representation learning for vision tasks. However, effectively adapting VLMs to downstream applications remains challenging, as their accuracy often depends on time-intensive and expertise-demanding prompt engineering, while full model fine-tuning is costly. This is particularly true for biomedical images, which, unlike natural images, typically suffer from limited annotated datasets, unintuitive image contrasts, and nuanced visual features. Recent prompt learning techniques, such as Context Optimization (CoOp) intend to tackle these issues, but still fall short in generalizability. Meanwhile, explorations in prompt learning for biomedical image analysis are still highly limited. In this work, we propose BiomedCoOp, a novel prompt learning framework that enables efficient adaptation of BiomedCLIP for accurate and highly generalizable few-shot biomedical image classification. Our approach achieves effective prompt context learning by leveraging semantic consistency with average prompt ensembles from Large Language Models (LLMs) and knowledge distillation with a statistics-based prompt selection strategy. We conducted comprehensive validation of our proposed framework on 11 medical datasets across 9 modalities and 10 organs against existing state-of-the-art methods, demonstrating significant improvements in both accuracy and generalizability.

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
Magnetic Resonance Imaging	Brain	BTMRI-P	Glioma Tumor, Meningioma Tumor, Normal Brain, Pituitary Tumor	8000/0/1000
Magnetic Resonance Imaging	Brain	BTMRI-S	Glioma Tumor, Meningioma Tumor, Normal Brain, Pituitary Tumor	2451/0/529
Magnetic Resonance Imaging	Brain	BRISC	Glioma Tumor, Meningioma Tumor, Normal Brain, Pituitary Tumor	2498/0/1000
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
Ultrasound	Breast	BUID	Benign Tumors, Malignant Tumors	162/0/36
Ultrasound	Breast	BUSBRA	Benign Tumors, Malignant Tumors	1311/0/283
Ultrasound	Breast	UDIAT	Benign Tumors, Malignant Tumors	113/0/26
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]

Domain Generalization Datasets

• BTMRI-P HuggingFace
• BTMRI-S HuggingFace
• BRISC HuggingFace
• BUID HuggingFace
• BUSBRA HuggingFace
• UDIAT 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:

@inproceedings{koleilat2025biomedcoop,
  title={Biomedcoop: Learning to prompt for biomedical vision-language models},
  author={Koleilat, Taha and Asgariandehkordi, Hojat and Rivaz, Hassan and Xiao, Yiming},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={14766--14776},
  year={2025}
}

@article{
koleilat2026clipsvd,
title={{CLIP}-{SVD}: Efficient and Interpretable Vision{\textendash}Language Adaptation via Singular Values},
author={Taha Koleilat and Hassan Rivaz and Yiming Xiao},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2026},
url={https://openreview.net/forum?id=XYy8pwqwMR}
}

@inproceedings{koleilat2026evisteer,
  author    = {Taha Koleilat and Hassan Rivaz and Yiming Xiao},
  title     = {Evi-Steer: Learning to Steer Biomedical Vision-Language Models Through Efficient and Generalizable Evidential Tuning},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
  pages     = {208--218},
  year      = {2027},
  publisher = {Springer Nature Switzerland}
}