3 citations · 11 across the 8 of their papers we have counts for
8 papers
"Nothing Abnormal": Disambiguating Medical Reports via Contrastive Knowledge Infusion
Zexue He, An Yan, Amilcare Gentili +2
Sharing medical reports is essential for patient-centered care. A recent line of work has focused on automatically generating reports with NLP methods. However, different audiences…
Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction
William Hogan, Molly Huang, Yannis Katsis +5
Relation extraction in the biomedical domain is a challenging task due to a lack of labeled data and a long-tail distribution of fact triples. Many works leverage distant supervisi…
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
An Yan, Zexue He, Xing Lu +5
Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretat…
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
Canlin Zhang, Chun-Nan Hsu, Yannis Katsis +2
Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provid…
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays
Jianmo Ni, Chun-Nan Hsu, Amilcare Gentili +1
Automatic medical image report generation has drawn growing attention due to its potential to alleviate radiologists' workload. Existing work on report generation often trains enco…
Antibody Watch: Text Mining Antibody Specificity from the Literature
Chun-Nan Hsu, Chia-Hui Chang, Thamolwan Poopradubsil +7
Antibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bin…
The Impact of Automatic Pre-annotation in Clinical Note Data Element Extraction - the CLEAN Tool
Tsung-Ting Kuo, Jina Huh, Jihoon Kim +8
Objective. Annotation is expensive but essential for clinical note review and clinical natural language processing (cNLP). However, the extent to which computer-generated pre-annot…
Deduplication in a massive clinical note dataset
Sanjeev Shenoy, Tsung-Ting Kuo, Rodney Gabriel +2
Duplication, whether exact or partial, is a common issue in many datasets. In clinical notes data, duplication (and near duplication) can arise for many reasons, such as the pervas…