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• MedSAD-CLIP achieves superior performance in pixel-level segmentation and image-level classification over state-of-the-art methods on four diverse medical benchmarks.result 90%
• Token-Patch Cross-Attention (TPCA) improves lesion localization while preserving CLIP's generalization capability.method 85%
• Lightweight image adapters and learnable prompt tokens efficiently adapt pretrained CLIP to the medical domain while preserving semantic alignment.method 85%
• A Margin-based image-text Contrastive Loss enhances global feature discrimination between normal and abnormal representations.technical 85%
• Recent CLIP-based studies for anomaly detection typically rely on global representations and weak supervision, producing coarse localization and limited segmentation quality.limitation 80%
• Supervised CLIP adaptation represents a unified and scalable paradigm for medical anomaly understanding.market 75%
• The approach works under a realistic clinical setting where limited yet meaningful labeled abnormal data is available.method 80%

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