A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERT
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913624404000768 |
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| author | Ganapathy, Nagarajan Chary, Podakanti Satyajith Pithani, Teja Venkata Ramana Kumar Kavati, Pavan S, Arun Kumar |
| author_facet | Ganapathy, Nagarajan Chary, Podakanti Satyajith Pithani, Teja Venkata Ramana Kumar Kavati, Pavan S, Arun Kumar |
| contents | This Paper presents an advanced approach for fine-tuning BiomedCLIP PubMedBERT, a multimodal model, to classify abnormalities in Video Capsule Endoscopy (VCE) frames, aiming to enhance diagnostic efficiency in gastrointestinal healthcare. By integrating the PubMedBERT language model with a Vision Transformer (ViT) to process endoscopic images, our method categorizes images into ten specific classes: angioectasia, bleeding, erosion, erythema, foreign body, lymphangiectasia, polyp, ulcer, worms, and normal. Our workflow incorporates image preprocessing and fine-tunes the BiomedCLIP model to generate high-quality embeddings for both visual and textual inputs, aligning them through similarity scoring for classification. Performance metrics, including classification, accuracy, recall, and F1 score, indicate the models strong ability to accurately identify abnormalities in endoscopic frames, showing promise for practical use in clinical diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERT Ganapathy, Nagarajan Chary, Podakanti Satyajith Pithani, Teja Venkata Ramana Kumar Kavati, Pavan S, Arun Kumar Computer Vision and Pattern Recognition This Paper presents an advanced approach for fine-tuning BiomedCLIP PubMedBERT, a multimodal model, to classify abnormalities in Video Capsule Endoscopy (VCE) frames, aiming to enhance diagnostic efficiency in gastrointestinal healthcare. By integrating the PubMedBERT language model with a Vision Transformer (ViT) to process endoscopic images, our method categorizes images into ten specific classes: angioectasia, bleeding, erosion, erythema, foreign body, lymphangiectasia, polyp, ulcer, worms, and normal. Our workflow incorporates image preprocessing and fine-tunes the BiomedCLIP model to generate high-quality embeddings for both visual and textual inputs, aligning them through similarity scoring for classification. Performance metrics, including classification, accuracy, recall, and F1 score, indicate the models strong ability to accurately identify abnormalities in endoscopic frames, showing promise for practical use in clinical diagnostics. |
| title | A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERT |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.19944 |