4 citations · 9 across the 9 of their papers we have counts for
6 papers · 1 filter
RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation
Yucheng Chen, Yang Yu, Yufei Shi +3
Radiology report generation (RRG) has emerged as a promising approach to alleviate radiologists' workload and reduce human errors by automatically generating diagnostic reports fro…
A Survey of Pathology Foundation Model: Progress and Future Directions
Conghao Xiong, Hao Chen, Joseph J. Y. Sung
Computational pathology, which involves analyzing whole slide images for automated cancer diagnosis, relies on multiple instance learning, where performance depends heavily on the…
Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classification
Conghao Xiong, Zhengrui Guo, Zhe Xu +6
Few-shot Whole Slide Image (WSI) classification is severely hampered by overfitting. We argue that this is not merely a data-scarcity issue but a fundamentally geometric problem. G…
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
Zhengrui Guo, Conghao Xiong, Jiabo Ma +4
Few-shot learning presents a critical solution for cancer diagnosis in computational pathology (CPath), addressing fundamental limitations in data availability, particularly the sc…
TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification
Conghao Xiong, Yi Lin, Hao Chen +5
Transferring knowledge from a source domain to a target domain can be crucial for whole slide image classification, since the number of samples in a dataset is often limited due to…
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification
Conghao Xiong, Hao Chen, Joseph J. Y. Sung +1
Multiple Instance Learning (MIL) and transformers are increasingly popular in histopathology Whole Slide Image (WSI) classification. However, unlike human pathologists who selectiv…