5 papers · 1 filter
SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images
Zhiling Yan, Sicheng Chen, Tianyi Zhang +3
Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is essential for diagnosis and treatment. However, acq…
MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis
Sicheng Chen, Chad Wong, Tianyi Zhang +3
Whole Slide Image (WSI) analysis is pivotal in computational pathology, enabling cancer diagnosis by integrating morphological and architectural cues across magnifications. Multipl…
SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
Enhui Chai, Sicheng Chen, Tianyi Zhang +2
Pathological diagnosis is highly reliant on image analysis, where Regions of Interest (ROIs) serve as the primary basis for diagnostic evidence, while whole-slide image (WSI)-level…
Generating Progressive Images from Pathological Transitions via Diffusion Model
Zeyu Liu, Tianyi Zhang, Yufang He +3
Deep learning is widely applied in computer-aided pathological diagnosis, which alleviates the pathologist workload and provide timely clinical analysis. However, most models gener…
CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification
Tianyi Zhang, Zhiling Yan, Chunhui Li +5
In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. To overcome this challenge, mixing-based methods have em…