Search Results for author: Yequan Bie

Found 7 papers, 3 papers with code

ConceptCLIP: Towards Trustworthy Medical AI via Concept-Enhanced Contrastive Langauge-Image Pre-training

1 code implementation26 Jan 2025 Yuxiang Nie, Sunan He, Yequan Bie, Yihui Wang, Zhixuan Chen, Shu Yang, Hao Chen

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.

Articles Concept Alignment +1

Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks

no code implementations CVPR 2025 Peng Xie, Yequan Bie, Jianda Mao, Yangqiu Song, Yang Wang, Hao Chen, Kani Chen

Pre-trained vision-language models (VLMs) have showcased remarkable performance in image and natural language understanding, such as image captioning and response generation.

Image Captioning Natural Language Understanding +1

Large Language Model with Region-guided Referring and Grounding for CT Report Generation

1 code implementation23 Nov 2024 Zhixuan Chen, Yequan Bie, Haibo Jin, Hao Chen

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.

Computed Tomography (CT) Diagnostic +5

Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

no code implementations3 Oct 2024 Junlin Hou, Sicen Liu, Yequan Bie, Hongmei Wang, Andong Tan, Luyang Luo, Hao Chen

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).

counterfactual Counterfactual Explanation +5

Dia-LLaMA: Towards Large Language Model-driven CT Report Generation

no code implementations25 Mar 2024 Zhixuan Chen, Luyang Luo, Yequan Bie, Hao Chen

Medical report generation has achieved remarkable advancements yet has still been faced with several challenges.

Diagnostic Language Modeling +4

XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization

no code implementations14 Mar 2024 Yequan Bie, Luyang Luo, Zhixuan Chen, Hao Chen

Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention.

Diagnostic Explainable artificial intelligence +4

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