8 citations · 16 across the 7 of their papers we have counts for
10 papers
SpecBraM: What Should an EEG Foundation Model Predict? Masked Band-Power Prediction versus Waveform Reconstruction
Peng Xie, Yequan Bie, Jianda Mao +1
Self-supervised EEG models often reconstruct masked waveforms or predict discrete codes. We study a task-aligned alternative: masked band-power prediction (MBP), which predicts fix…
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…
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…
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…
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
Zhixuan Chen, Luyang Luo, Yequan Bie +1
Medical report generation has achieved remarkable advancements yet has still been faced with several challenges. First, the inherent imbalance in the distribution of normal and abn…
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Yequan Bie, Luyang Luo, Zhixuan Chen +1
Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention. Within this research field, s…
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
Yequan Bie, Luyang Luo, Hao Chen
Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis. However, the stringent trustworthiness requirements intrinsic to the…