Automated Spinal MRI Labelling from Reports Using a Large Language Model

• URL: http://arxiv.org/abs/2410.17235v1
• Date: Tue, 22 Oct 2024 17:54:07 GMT
• Title: Automated Spinal MRI Labelling from Reports Using a Large Language Model
• Authors: Robin Y. Park, Rhydian Windsor, Amir Jamaludin, Andrew Zisserman,
• Abstract summary: We propose a pipeline to automate the extraction of labels from radiology reports using large language models. Our method equals or surpasses GPT-4 on a held-out set of reports. We show that the extracted labels can be used to train imaging models to classify the identified conditions in the accompanying MR scans.
• Score: 45.348320669329205
• License:
• Abstract: We propose a general pipeline to automate the extraction of labels from radiology reports using large language models, which we validate on spinal MRI reports. The efficacy of our labelling method is measured on five distinct conditions: spinal cancer, stenosis, spondylolisthesis, cauda equina compression and herniation. Using open-source models, our method equals or surpasses GPT-4 on a held-out set of reports. Furthermore, we show that the extracted labels can be used to train imaging models to classify the identified conditions in the accompanying MR scans. All classifiers trained using automated labels achieve comparable performance to models trained using scans manually annotated by clinicians. Code can be found at https://github.com/robinyjpark/AutoLabelClassifier.

Related papers

• Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification [0.6144680854063935]
  In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. We present MAPLEZ, a novel approach leveraging a locally executable Large Language Model (LLM) to extract and enhance findings labels.
  arXiv  Detail & Related papers  (2024-03-06T20:10:41Z)
• LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories [59.14011485494713]
  This work introduces a fully automated 2D/3D labeling framework that can generate labels for RGB-D scans at equal (or better) level of accuracy. We demonstrate the effectiveness of our LabelMaker pipeline by generating significantly better labels for the ScanNet datasets and automatically labelling the previously unlabeled ARKitScenes dataset.
  arXiv  Detail & Related papers  (2023-11-20T20:40:24Z)
• Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning [50.591267188664666]
  We propose a deep learning-based CheXpert label prediction model, pre-trained on reports labeled by a rule-based German CheXpert model. Our results demonstrate the effectiveness of our approach, which significantly outperformed the rule-based model on all three tasks.
  arXiv  Detail & Related papers  (2023-06-09T16:08:35Z)
• German CheXpert Chest X-ray Radiology Report Labeler [50.591267188664666]
  This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports. Results showed that automated label extraction can reduce time spent on manual labeling and improve overall modeling performance.
  arXiv  Detail & Related papers  (2023-06-05T11:01:58Z)
• BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification [25.76256302330625]
  New medical imaging classification problems may need to rely on machine-generated noisy labels extracted from radiology reports. Current noisy-label learning methods designed for multi-class problems cannot be easily adapted. We propose a new method designed for the noisy multi-label CXR learning, which detects and smoothly re-labels samples from the dataset.
  arXiv  Detail & Related papers  (2022-03-03T08:04:59Z)
• Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images [83.7047542725469]
  Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need of external training data. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming the state-of-the-art alternatives, even while learning from a single slide.
  arXiv  Detail & Related papers  (2021-09-22T15:06:06Z)
• Paying Per-label Attention for Multi-label Extraction from Radiology Reports [1.9601378412924186]
  We tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients. We propose a set of 31 labels which correspond to radiographic findings and clinical impressions related to neurological abnormalities. We are able to robustly extract many labels with a single model, classified according to the radiologist's reporting.
  arXiv  Detail & Related papers  (2020-07-31T16:11:09Z)
• Chest X-ray Report Generation through Fine-Grained Label Learning [46.352966049776875]
  We present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings.
  arXiv  Detail & Related papers  (2020-07-27T19:50:56Z)

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.