3 citations · 3 across the 4 of their papers we have counts for
4 papers
Contribution-Guided Asymmetric Learning for Robust Multimodal Fusion under Imbalance and Noise
Zijing Xu, Yunfeng Kou, Kunming Wu +1
Multimodal learning faces two major challenges: modality imbalance and data noise, which significantly affect the robustness and generalization ability of models. Existing methods…
Adaptive Prototype Learning for Multimodal Cancer Survival Analysis
Hong Liu, Haosen Yang, Federica Eduati +2
Leveraging multimodal data, particularly the integration of whole-slide histology images (WSIs) and transcriptomic profiles, holds great promise for improving cancer survival predi…
PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting
Hong Liu, Haosen Yang, Evi M. C. Huijben +4
Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained image-mask pairs annotated by pathologists. Thus, synthesizing histopathology data…
WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images
Hong Liu, Haosen Yang, Paul J. van Diest +2
The Segment Anything Model (SAM) marks a significant advancement in segmentation models, offering robust zero-shot abilities and dynamic prompting. However, existing medical SAMs a…