Search Results for author: Chun-Nan Hsu

Found 15 papers, 3 papers with code

Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models

no code implementations4 Oct 2023 An Yan, Yu Wang, Yiwu Zhong, Zexue He, Petros Karypis, Zihan Wang, chengyu dong, Amilcare Gentili, Chun-Nan Hsu, Jingbo Shang, Julian McAuley

Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients.

Image Classification Language Modelling +1

"Nothing Abnormal": Disambiguating Medical Reports via Contrastive Knowledge Infusion

no code implementations15 May 2023 Zexue He, An Yan, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu

Based on our analysis, we define a disambiguation rewriting task to regenerate an input to be unambiguous while preserving information about the original content.

SPOT: Knowledge-Enhanced Language Representations for Information Extraction

no code implementations20 Aug 2022 Jiacheng Li, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Andrew Bartko, Julian McAuley, Chun-Nan Hsu

To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively.

Relation Extraction

Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction

1 code implementation AKBC 2021 William Hogan, Molly Huang, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Yoshiki Vazquez Baeza, Andrew Bartko, Chun-Nan Hsu

In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types.

Relation Relation Extraction

Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation

1 code implementation Findings (EMNLP) 2021 An Yan, Zexue He, Xing Lu, Jiang Du, Eric Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu

Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation.

Contrastive Learning Descriptive +2

Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery

no code implementations12 Nov 2020 Canlin Zhang, Chun-Nan Hsu, Yannis Katsis, Ho-Cheol Kim, Yoshiki Vazquez-Baeza

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 provide new ways to approach some Natural Language Processing research topics.

Link Prediction

Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays

no code implementations Findings of the Association for Computational Linguistics 2020 Jianmo Ni, Chun-Nan Hsu, Amilcare Gentili, Julian McAuley

In this work, we focus on reporting abnormal findings on radiology images; instead of training on complete radiology reports, we propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules.

Clustering Cross-Modal Retrieval +2

Antibody Watch: Text Mining Antibody Specificity from the Literature

1 code implementation5 Aug 2020 Chun-Nan Hsu, Chia-Hui Chang, Thamolwan Poopradubsil, Amanda Lo, Karen A. William, Ko-Wei Lin, Anita Bandrowski, Ibrahim Burak Ozyurt, Jeffrey S. Grethe, Maryann E. Martone

Given an input article, the first task is to identify snippets about antibody specificity and classify if the snippets report that any antibody exhibits non-specificity, and thus is problematic.

Specificity

NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction

no code implementations AKBC 2019 Dustin Wright, Yannis Katsis, Raghav Mehta, Chun-Nan Hsu

Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.

Word Embeddings

The Impact of Automatic Pre-annotation in Clinical Note Data Element Extraction - the CLEAN Tool

no code implementations11 Aug 2018 Tsung-Ting Kuo, Jina Huh, Ji-Hoon Kim, Robert El-Kareh, Siddharth Singh, Stephanie Feudjio Feupe, Vincent Kuri, Gordon Lin, Michele E. Day, Lucila Ohno-Machado, Chun-Nan Hsu

Our study introduces CLEAN (CLinical note rEview and ANnotation), a pre-annotation-based cNLP annotation system to improve clinical note annotation of data elements, and comprehensively compares CLEAN with the widely-used annotation system Brat Rapid Annotation Tool (BRAT).

Open-Ended Question Answering

Periodic Step Size Adaptation for Single Pass On-line Learning

no code implementations NeurIPS 2009 Chun-Nan Hsu, Yu-Ming Chang, Hanshen Huang, Yuh-Jye Lee

It has been established that the second-order stochastic gradient descent (2SGD) method can potentially achieve generalization performance as well as empirical optimum in a single pass (i. e., epoch) through the training examples.

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