110 citations · 114 across the 7 of their papers we have counts for
7 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…
PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learning
Fuqiang Chen, Ranran Zhang, Wanming Hu +6
Immunohistochemical (IHC) staining enables precise molecular profiling of protein expression, with over 200 clinically available antibody-based tests in modern pathology. However,…
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…
USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining
Yue Peng, Bing Xiong, Fuqiang Chen +5
Immunohistochemical (IHC) virtual staining is a task that generates virtual IHC images from H\&E images while maintaining pathological semantic consistency with adjacent slices. Th…
Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion
Bing Xiong, Yue Peng, RanRan Zhang +3
Virtual staining leverages computer-aided techniques to transfer the style of histochemically stained tissue samples to other staining types. In virtual staining of pathological im…
Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining
Fuqiang Chen, Ranran Zhang, Boyun Zheng +3
Conventional hematoxylin-eosin (H&E) staining is limited to revealing cell morphology and distribution, whereas immunohistochemical (IHC) staining provides precise and specific vis…
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Chetan L. Srinidhi, Seung Wook Kim, Fu-Der Chen +1
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often…