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Found 7 papers, 3 papers with code
ConceptCLIP: Towards Trustworthy Medical AI via Concept-Enhanced Contrastive Langauge-Image Pre-training
This dual alignment strategy enhances the model's capability to associate specific image regions with relevant concepts, thereby improving both the precision of analysis and the interpretability of the AI system.
Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks
Pre-trained vision-language models (VLMs) have showcased remarkable performance in image and natural language understanding, such as image captioning and response generation.
Large Language Model with Region-guided Referring and Grounding for CT Report Generation
It leverages the recognition of referring regions to guide the generation of region-specific reports, enhancing the model's referring and grounding capabilities while also improving the report's interpretability.
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI).
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
Medical report generation has achieved remarkable advancements yet has still been faced with several challenges.
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention.
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis.
