ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records-Reference-Cited by-同舟云学术

ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records

Published:2022-12-17 Issue: Volume: Page:
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Container-title:2022 IEEE International Conference on Big Data (Big Data)
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Author:

Wei Qiang¹,Zuo Xu¹,Anjum Omer¹,Hu Yan¹,Denlinger Ryan¹,Bernstam Elmer V.²,Citardi Martin J³,Xu Hua¹

Affiliation:

1. The University of Texas Health Science Center at Houston,School of Biomedical Informatics,Houston,USA

2. The University of Texas Health Science Center at Houston,School of Biomedical Informatics Division of General Internal Medicine, McGovern Medical School,Houston,USA

3. The University of Texas Health Science Center at Houston,McGovern Medical School,Department of Otorhinolaryngology-Head & Neck Surgery,Houston,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020569.pdf?arnumber=10020569

Reference38 articles.

1. TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

2. SpanBERT: Improving Pre-training by Representing and Predicting Spans

3. Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Models

4. DiT: Selfsupervised Pre-training for Document Image Transformer;li,2022

5. LayoutLMv3: Pretraining for Document AI with Unified Text and Image Masking;huang,2022

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