26 citations · 51 across the 12 of their papers we have counts for
7 papers · 1 filter
Toward expanding the scope of radiology report summarization to multiple anatomies and modalities
Zhihong Chen, Maya Varma, Xiang Wan +2
Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression sectio…
Improving Radiology Summarization with Radiograph and Anatomy Prompts
Jinpeng Hu, Zhihong Chen, Yang Liu +2
The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists. To alleviate the workload of…
Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge
Zhihong Chen, Guanbin Li, Xiang Wan
Medical vision-and-language pre-training (Med-VLP) has received considerable attention owing to its applicability to extracting generic vision-and-language representations from med…
Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
Zhihong Chen, Yuhao Du, Jinpeng Hu +4
Medical vision-and-language pre-training provides a feasible solution to extract effective vision-and-language representations from medical images and texts. However, few studies h…
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
Yang Liu, Jinpeng Hu, Xiang Wan +1
Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. Some recent works have…
Hero-Gang Neural Model For Named Entity Recognition
Jinpeng Hu, Yaling Shen, Yang Liu +2
Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text. Recently, since the multi-head attention mecha…
Graph Enhanced Contrastive Learning for Radiology Findings Summarization
Jinpeng Hu, Zhuo Li, Zhihong Chen +3
The impression section of a radiology report summarizes the most prominent observation from the findings section and is the most important section for radiologists to communicate t…