Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval

Preprint:

https://www.arxiv.org/abs/2409.09430

Dataset: a subset of MedMNIST V2

Feature extractors:

• VGG19
• ResNet50
• DenseNet121
• EfficientNetV2M
• MedCLIP
• BioMedClip
• OpenCLIP
• CONCH
• UNI

Similarity:

Cosine similarity index

Evaluation:

• mAP@5
• MMV@5
• ACC@1
• ACC@3
• ACC@5

Results:

• Main results in the manuscript
• More detailed results are available in "Supplementary Materials"
• Selective results based on ACC@1:

ACC@1 for different datasets based on the models and input image size

How to run:

• For pre-trained CNNs for 2D datasets run main_2D_CNN.py
• For pre-trained CNNs for 2D datasets (if your memory is limited to load the entire data) run main_2D_CNN_memory_safe
• For pre-trained foundation models for 2D datasets run main_2D_foundation.py
• For pre-trained foundation models for 2D datasets (if your memory is limited to load the entire data) run main_2D_foundation_memory_safe
• For pre-trained models (CNN or foundation) for 3D datasets run main_3D.py
• To create bar charts run plot.py

Used repositories/sources:

• For CNNs: https://www.tensorflow.org/api_docs/python/tf/keras/applications
• For MedCLIP: https://github.com/RyanWangZf/MedCLIP
• For BioMedCLIP: https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224/blob/main/biomed_clip_example.ipynb
• For OpenCLIP: https://github.com/mlfoundations/open_clip
• For CONCH: https://github.com/mahmoodlab/CONCH
• For UNI: https://github.com/mahmoodlab/UNI

Citation:

@article{mahbod2024evaluating,
  title={Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval},
  author={Mahbod, Amirreza and Saeidi, Nematollah and Hatamikia, Sepideh and Woitek, Ramona},
  journal={arXiv preprint arXiv:2409.09430},
  year={2024}
}

Contact:

Amirreza Mahbod [email protected]

Main References:

• VGG19: Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
• ResNet50: He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
• DenseNet121: Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
• EfficientNetV2M: Tan, Mingxing, and Quoc Le. "Efficientnetv2: Smaller models and faster training." International conference on machine learning. PMLR, 2021.
• MedCLIP: Wang, Zifeng, et al. "Medclip: Contrastive learning from unpaired medical images and text." arXiv preprint arXiv:2210.10163 (2022).
• BioMedClip: Zhang, Sheng, et al. "BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs." arXiv preprint arXiv:2303.00915 (2023).
• OpenCLIP: Cherti, Mehdi, et al. "Reproducible scaling laws for contrastive language-image learning." Proceedings of the IEEE/CVF Conference on - Computer Vision and Pattern Recognition. 2023.
• CONCH: Lu, Ming Y., et al. "Visual language pretrained multiple instance zero-shot transfer for histopathology images." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.
• UNI: Chen, Richard J., et al. "Towards a general-purpose foundation model for computational pathology." Nature Medicine 30.3 (2024): 850-862.