9 citations · 18 across the 13 of their papers we have counts for
13 papers
ContiStain: Cross-Domain Relation-Preserving Distillation for Continual Multi-Domain Virtual IHC Staining
Fuqiang Chen, Yifeng Wang, Hongpeng Wang +1
A unified multiplex virtual staining model enables scalable and non-destructive multiplex analysis from H&E slides while promoting parameter efficiency, shared pathological knowled…
Spectral Property-Driven Data Augmentation for Hyperspectral Single-Source Domain Generalization
Taiqin Chen, Yifeng Wang, Xiaochen Feng +4
While hyperspectral images (HSI) benefit from numerous spectral channels that provide rich information for classification, the increased dimensionality and sensor variability make…
A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis
Xianchao Guan, Zhiyuan Fan, Yifeng Wang +11
The development of clinical-grade artificial intelligence in pathology is limited by the scarcity of diverse, high-quality annotated datasets. Generative models offer a potential s…
The Four Color Theorem for Cell Instance Segmentation
Ye Zhang, Yu Zhou, Yifeng Wang +4
Cell instance segmentation is critical to analyzing biomedical images, yet accurately distinguishing tightly touching cells remains a persistent challenge. Existing instance segmen…
Category Prompt Mamba Network for Nuclei Segmentation and Classification
Ye Zhang, Zijie Fang, Yifeng Wang +3
Nuclei segmentation and classification provide an essential basis for tumor immune microenvironment analysis. The previous nuclei segmentation and classification models require spl…
HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization
Zijie Fang, Yifeng Wang, Peizhang Xie +2
Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid the expensive and laborious acquisition of pixel-level annotations, a wide range of studie…
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images
Songhan Jiang, Zhengyu Gan, Linghan Cai +2
Survival prediction, utilizing pathological images and genomic profiles, is increasingly important in cancer analysis and prognosis. Despite significant progress, precise survival…
Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation
Ziyue Wang, Ye Zhang, Yifeng Wang +2
Deep learning has achieved impressive results in nuclei segmentation, but the massive requirement for pixel-wise labels remains a significant challenge. To alleviate the annotation…
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
Ye Zhang, Yifeng Wang, Zijie Fang +4
Weakly supervised segmentation methods have gained significant attention due to their ability to reduce the reliance on costly pixel-level annotations during model training. Howeve…
MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models
Zijie Fang, Yifeng Wang, Ye Zhang +4
Recently, pathological diagnosis has achieved superior performance by combining deep learning models with the multiple instance learning (MIL) framework using whole slide images (W…
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
Ye Zhang, Ziyue Wang, Yifeng Wang +5
Semi-supervised segmentation methods have demonstrated promising results in natural scenarios, providing a solution to reduce dependency on manual annotation. However, these method…
Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification
Zijie Fang, Yihan Liu, Yifeng Wang +10
Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and comple…
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image
Zhuchen Shao, Liuxi Dai, Yifeng Wang +2
Multiple Instance Learning (MIL), a powerful strategy for weakly supervised learning, is able to perform various prediction tasks on gigapixel Whole Slide Images (WSIs). However, t…