2 citations · 3 across the 7 of their papers we have counts for
7 papers · 1 filter
Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis
Kunming Tang, Zhiguo Jiang, Jun Shi +3
Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are e…
SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification
Tong Shu, Jun Shi, Dongdong Sun +2
Existing WSI analysis methods lie on the consensus that histopathological characteristics of tumors are significant guidance for cancer diagnostics. Particularly, as the evolution…
Lifelong Histopathology Whole Slide Image Retrieval via Distance Consistency Rehearsal
Xinyu Zhu, Zhiguo Jiang, Kun Wu +2
Content-based histopathological image retrieval (CBHIR) has gained attention in recent years, offering the capability to return histopathology images that are content-wise similar…
Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification
Saisai Ding, Juncheng Li, Jun Wang +2
The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness…
SANDFORMER: CNN and Transformer under Gated Fusion for Sand Dust Image Restoration
Jun Shi, Bingcai Wei, Gang Zhou +1
Although Convolutional Neural Networks (CNN) have made good progress in image restoration, the intrinsic equivalence and locality of convolutions still constrain further improvemen…
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification
Yushan Zheng, Jun Li, Jun Shi +2
Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wi…