5 papers
Dual-stream attention-guided learning for weakly supervised whole slide image classification
Daoxi Cao, Hangbei Cheng, Yijin Li +8
Whole slide images (WSIs) play a crucial role in cancer diagnosis due to their ultra-high resolution and rich morphological information, and multiple instance learning (MIL) has be…
Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decoding
Feixue Shao, Guangze Shi, Xueyu Liu +6
Visual decoding of neurophysiological signals is a critical challenge for brain-computer interfaces (BCIs) and computational neuroscience. However, current approaches are often con…
Attack for Defense: Adversarial Agents for Point Prompt Optimization Empowering Segment Anything Model
Xueyu Liu, Xiaoyi Zhang, Guangze Shi +4
Prompt quality plays a critical role in the performance of the Segment Anything Model (SAM), yet existing approaches often rely on heuristic or manually crafted prompts, limiting s…
D4PM: A Dual-branch Driven Denoising Diffusion Probabilistic Model with Joint Posterior Diffusion Sampling for EEG Artifacts Removal
Feixue Shao, Xueyu Liu, Yongfei Wu +3
Artifact removal is critical for accurate analysis and interpretation of Electroencephalogram (EEG) signals. Traditional methods perform poorly with strong artifact-EEG correlation…
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
Hangbei Cheng, Xiaorong Dong, Xueyu Liu +6
Accurate lesion segmentation in histopathology images is essential for diagnostic interpretation and quantitative analysis, yet it remains challenging due to the limited availabili…