9 papers
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…
PuzzleTuning: Explicitly Bridge Pathological and Natural Image with Puzzles
Tianyi Zhang, Shangqing Lyu, Yanli Lei +7
Pathological image analysis is a crucial field in computer vision. Due to the annotation scarcity in the pathological field, pre-training with self-supervised learning (SSL) is wid…
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
Nan Ying, Yanli Lei, Tianyi Zhang +6
Pathological image analysis is a crucial field in computer-aided diagnosis, where deep learning is widely applied. Transfer learning using pre-trained models initialized on natural…
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…
Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification
Tianyi Zhang, Youdan Feng, Yunlu Feng +6
The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained cytopathological images. C…
Pancreatic Cancer ROSE Image Classification Based on Multiple Instance Learning with Shuffle Instances
Tianyi Zhang, Youdan Feng, Yunlu Feng +1
The rapid on-site evaluation (ROSE) technique can significantly ac-celerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological…