Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation

• URL: http://arxiv.org/abs/2407.01948v1
• Date: Tue, 2 Jul 2024 04:39:19 GMT
• Title: Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
• Authors: Pablo Messina, René Vidal, Denis Parra, Álvaro Soto, Vladimir Araujo,
• Abstract summary: We present a novel two-stage framework designed to extract high-quality factual statements from free-text radiology reports. Our framework also includes a new embedding-based metric ( CXRFE) for evaluating chest X-ray text generation systems.
• Score: 31.370503681645804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. To tackle this issue, we present a novel two-stage framework designed to extract high-quality factual statements from free-text radiology reports in order to improve the representations of text encoders and, consequently, their performance on various downstream tasks. In the first stage, we propose a \textit{Fact Extractor} that leverages large language models (LLMs) to identify factual statements from well-curated domain-specific datasets. In the second stage, we introduce a \textit{Fact Encoder} (CXRFE) based on a BERT model fine-tuned with objective functions designed to improve its representations using the extracted factual data. Our framework also includes a new embedding-based metric (CXRFEScore) for evaluating chest X-ray text generation systems, leveraging both stages of our approach. Extensive evaluations show that our fact extractor and encoder outperform current state-of-the-art methods in tasks such as sentence ranking, natural language inference, and label extraction from radiology reports. Additionally, our metric proves to be more robust and effective than existing metrics commonly used in the radiology report generation literature. The code of this project is available at \url{https://github.com/PabloMessina/CXR-Fact-Encoder}.

Related papers

• LOGO: Video Text Spotting with Language Collaboration and Glyph Perception Model [20.007650672107566]
  Video text spotting (VTS) aims to simultaneously localize, recognize and track text instances in videos. Recent methods track the zero-shot results of state-of-the-art image text spotters directly. Fine-tuning transformer-based text spotters on specific datasets could yield performance enhancements.
  arXiv  Detail & Related papers  (2024-05-29T15:35:09Z)
• Cheap Ways of Extracting Clinical Markers from Texts [0.0]
  This paper describes the work of the UniBuc Archaeology team for CLPsych's 2024 Shared Task. It involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights and summaries.
  arXiv  Detail & Related papers  (2024-03-17T14:21:42Z)
• Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition [51.66383337087724]
  The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR. Recent studies have demonstrated that incorporating bidirectional context information significantly improves the performance of HMER models. We propose the Mirror-Flipped Symbol Layout Tree (MF-SLT) and Bidirectional Asynchronous Training (BAT) structure.
  arXiv  Detail & Related papers  (2023-12-31T09:24:21Z)
• Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection [71.20871905457174]
  Language models (LMs) have revolutionized the way we interact with information, but they often generate nonfactual text. Previous methods use external knowledge as references for text generation to enhance factuality but often struggle with the knowledge mix-up of irrelevant references. We present DKGen, which divide the text generation process into an iterative process.
  arXiv  Detail & Related papers  (2023-08-30T02:22:40Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT [80.33783969507458]
  The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Recent studies have achieved promising results in automatic impression generation using large-scale medical text data. These models often require substantial amounts of medical text data and have poor generalization performance.
  arXiv  Detail & Related papers  (2023-04-17T17:13:42Z)
• Radiology Text Analysis System (RadText): Architecture and Evaluation [21.051601364891418]
  RadText is an open-source radiology text analysis system developed by Python. It offers an easy-to-use text analysis pipeline, including de-identification, section segmentation, sentence split and word tokenization. It supports raw text processing and local processing, which enables better usability and improved data privacy.
  arXiv  Detail & Related papers  (2022-03-19T17:16:12Z)
• SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition [73.61592015908353]
  We propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism. The design results in a concise framework that requires neither additional rectification module nor character-level annotation.
  arXiv  Detail & Related papers  (2022-03-19T01:14:42Z)
• A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications [0.02578242050187029]
  This paper presents two datasets comprised of artificially generated research content. In the first case, the content is completely generated by the GPT-2 model after a short prompt extracted from original papers. The partial or hybrid dataset is created by replacing several sentences of abstracts with sentences that are generated by the Arxiv-NLP model. We evaluate the quality of the datasets comparing the generated texts to aligned original texts using fluency metrics such as BLEU and ROUGE.
  arXiv  Detail & Related papers  (2022-02-04T08:16:56Z)
• Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
  Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages. We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
  arXiv  Detail & Related papers  (2021-11-04T04:39:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.