1 citations · 1 across the 4 of their papers we have counts for
4 papers
Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View
Jianan Fan, Dongnan Liu, Canran Li +5
Cellular nuclei recognition serves as a fundamental and essential step in the workflow of digital pathology. However, with disparate source organs and staining procedures among his…
Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling
Jianan Fan, Dongnan Liu, Hang Chang +3
Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of…
Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation
Jianan Fan, Dongnan Liu, Hang Chang +1
Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles hindering the application of deep learning models for nuclei analysis, which holds a b…
Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation
Jianan Fan, Dongnan Liu, Hang Chang +3
The success of automated medical image analysis depends on large-scale and expert-annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a promising appro…