6 papers
Segment Anything in Pathology Images with Natural Language
Zhixuan Chen, Junlin Hou, Liqi Lin +6
Pathology image segmentation is crucial in computational pathology for analyzing histological features relevant to cancer diagnosis and prognosis. However, current methods face maj…
SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset
Peng Xie, Xingyuan Liu, Tsz Wai Chan +5
Code-switching (CS) is the alternating use of two or more languages within a conversation or utterance, often influenced by social context and speaker identity. This linguistic phe…
Large Language Model with Region-guided Referring and Grounding for CT Report Generation
Zhixuan Chen, Yequan Bie, Haibo Jin +1
Computed tomography (CT) report generation is crucial to assist radiologists in interpreting CT volumes, which can be time-consuming and labor-intensive. Existing methods primarily…
An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training
Yuxiang Nie, Sunan He, Yequan Bie +14
The clinical adoption of artificial intelligence (AI) in medical imaging requires models that are both diagnostically accurate and interpretable to clinicians. While current multim…
Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks
Peng Xie, Yequan Bie, Jianda Mao +4
Pre-trained vision-language models (VLMs) have showcased remarkable performance in image and natural language understanding, such as image captioning and response generation. As th…
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
Junlin Hou, Sicen Liu, Yequan Bie +4
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…