1 code implementation • 26 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.
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.
1 code implementation • 23 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.
no code implementations • 3 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).
no code implementations • 25 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.
no code implementations • 14 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.
1 code implementation • 16 Jan 2024 • Yequan Bie, Luyang Luo, Hao Chen
Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis.