3 citations · 7 across the 5 of their papers we have counts for
5 papers
PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology
Xiaomin Wu, Rui Xu, Pengchen Wei +4
Pathological diagnosis remains the definitive standard for identifying tumors. The rise of multimodal large models has simplified the process of integrating image analysis with tex…
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training
Rui Xu, Wenkang Qin, Peixiang Huang +2
Deep Neural Networks (DNNs) are expected to provide explanation for users to understand their black-box predictions. Saliency map is a common form of explanation illustrating the h…
Improving Vision-and-Language Reasoning via Spatial Relations Modeling
Cheng Yang, Rui Xu, Ye Guo +5
Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires high-level cognition and commonsense reasoning ability about the real world. In recent years, l…
What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning
Wenkang Qin, Rui Xu, Peixiang Huang +3
Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-aided pathological diagnosis, has rarely been studied due to the limitations in datasets and m…
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology
Peixiang Huang, Songtao Zhang, Yulu Gan +6
Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multip…