16 citations · 20 across the 5 of their papers we have counts for
7 papers · 1 filter
PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts
Bowen Liu, Qixiang Zhang, Xiaomeng Li
Whole-slide pathology reasoning requires models to integrate gigapixel-scale visual evidence across complete case-linked slides, yet current question-answering benchmarks primarily…
A Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
Jingyu Lu, Haonan Wang, Qixiang Zhang +1
Subject-agnostic brain decoding, which aims to reconstruct continuous visual experiences from fMRI without subject-specific training, holds great potential for clinical application…
ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding
Haonan Wang, Jingyu Lu, Hongrui Li +1
Recent advances in neural decoding have enabled the reconstruction of visual experiences from brain activity, positioning fMRI-to-image reconstruction as a promising bridge between…
Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks
Haonan Wang, Jiaji Mao, Lehan Wang +11
Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains time-consuming and expertise-int…
Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction
Haonan Wang, Qixiang Zhang, Lehan Wang +2
Decoding visual stimuli from neural activity is essential for understanding the human brain. While fMRI methods have successfully reconstructed static images, fMRI-to-video reconst…
S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentation
Qixiang Zhang, Haonan Wang, Xiaomeng Li
Semi-supervised medical image segmentation (SSMIS) has emerged as a promising solution to tackle the challenges of time-consuming manual labeling in the medical field. However, in…