4 citations · 5 across the 6 of their papers we have counts for
7 papers
Cell as Point: One-Stage Framework for Efficient Cell Tracking
Yaxuan Song, Jianan Fan, Heng Huang +2
Conventional multi-stage cell tracking approaches rely heavily on detection or segmentation in each frame as a prerequisite, requiring substantial resources for high-quality segmen…
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…
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…
DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis
Tiange Xiang, Yang Song, Chaoyi Zhang +6
We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classificati…
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
Dongnan Liu, Donghao Zhang, Yang Song +5
Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift…