3 citations · 3 across the 3 of their papers we have counts for
4 papers
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…
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…
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…