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q-bio.QM2024★ 1 cited
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…
cs.LG2024
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…
cs.CV2024
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…