4 citations · 9 across the 9 of their papers we have counts for
9 papers
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…
ConSurv: Multimodal Continual Learning for Survival Analysis
Dianzhi Yu, Conghao Xiong, Yankai Chen +6
Survival prediction of cancers is crucial for clinical practice, as it informs mortality risks and influences treatment plans. However, a static model trained on a single dataset f…
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…
MoME: Mixture of Multimodal Experts for Cancer Survival Prediction
Conghao Xiong, Hao Chen, Hao Zheng +4
Survival analysis, as a challenging task, requires integrating Whole Slide Images (WSIs) and genomic data for comprehensive decision-making. There are two main challenges in this t…
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…
Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry
Kelly Marchisio, Conghao Xiong, Philipp Koehn
A popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. New me…