Demo for UniMed-CLIP Medical VLMs
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Unimed Clip Medical Image Zero Shot Classification is a cutting-edge demo application designed for medical image classification using advanced Vision-Language Models (VLMs). Built on the UniMed-CLIP framework, it leverages zero-shot learning to classify medical images without requiring task-specific training data. This tool is particularly useful for healthcare professionals and researchers to quickly analyze and classify medical images into predefined categories.
• Zero-Shot Learning Capability: Classify medical images without task-specific training data.
• Support for Multiple Medical Image Types: Works with commonly used medical imaging formats such as X-rays, CT scans, and MRIs.
• High Accuracy: Utilizes state-of-the-art models for precise image classification.
• User-Friendly Interface: Designed for easy integration into existing workflows and systems.
• Customizable: Allows for fine-tuning with custom datasets or labels for specific use cases.
What is zero-shot learning?
Zero-shot learning enables the model to classify images into classes it has never seen during training, leveraging the contextual knowledge it has gained from large-scale pre-training.
Which types of medical images does it support?
The tool supports a variety of medical imaging formats, including X-rays, CT scans, MRI scans, and ultrasonography images.
Can I customize the classification labels?
Yes, you can customize the classification labels by fine-tuning the model with your own dataset or by providing custom labels during inference.