Supervised and unsupervised language modelling in Chest X-Ray radiological reports

Abstract

Chest radiography (CXR) is the most commonly used imaging modality and deep neural network (DNN) algorithms have shown promise in effective triage of normal and abnormal radiograms. Typically, DNNs require large quantities of expertly labelled training exemplars, which in clinical contexts is a major bottleneck to effective modelling, as both considerable clinical skill and time is required to produce high-quality ground truths. In this work we evaluate thirteen supervised classifiers using two large free-text corpora and demonstrate that bidirectional long short-term memory (BiLSTM) networks with attention mechanism effectively identify Normal, Abnormal, and Unclear CXR reports in internal (n = 965 manually-labelled reports, f1-score = 0.94) and external (n = 465 manually-labelled reports, f1-score = 0.90) testing sets using a relatively small number of expert-labelled training observations (n = 3,856 annotated reports). Furthermore, we introduce a general unsupervised approach that accurately distinguishes Normal and Abnormal CXR reports in a large unlabelled corpus. We anticipate that the results presented in this work can be used to automatically extract standardized clinical information from free-text CXR radiological reports, facilitating the training of clinical decision support systems for CXR triage.

Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

“…More recently, Transformer-based models have also been applied to the task of radiology report labeling. Drozdov et al (2020) trained classifiers using BERT (Devlin et al, 2019) and XLNet (Yang et al, 2020) on 3,856 radiologist labeled reports to detect normal and abnormal labels. Wood et al (2020) developed ALARM, an MRI head report classifier on head MRI data using BioBERT models trained on 1500 radiologist-labeled reports, and demonstrate improvement over simpler fixed embedding and word2vec-based (Mikolov et al, 2013) models (Zech et al, 2018).…”

“…More recently, Transformers have demonstrated success in end-to-end radiology report labeling (Drozdov et al, 2020;Wood et al, 2020). However, these methods have shifted the burden from feature engineering to manual annotation, requiring considerable time and expertise for high quality.…”

Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

“…Simple approaches have been demonstrated using word embeddings or bag of words feature representations followed by logistic regression [12] or decision trees [13]. More complex approaches using a variety of neural networks have been shown to be effective for document classification by many authors [14,15], especially with the addition of attention mechanisms [3,5,[16][17][18][19]. State-of-the-art solutions use existing pre-trained models, such as Bidirectional Encoder Representations from Transformers (BERT) [4], that have learnt underlying language patterns, and fine-tune them on small domain-specific datasets.…”

Automatic recognition of abdominal lymph nodes from clinical text

“…Chen et al applied CNNs to classify pulmonary embolism in chest CT reports (Chen et al, 2018). Drozdov et al compared thirteen supervised classifiers and demonstrate that bidirectional long short-term memory (BiLSTM) networks with attention mechanisms effectively identify labels in CXR reports (Drozdov et al, 2020). Wood et al present a transformer-based network for brain magnetic resonance imaging (MRI) radiology report classification, which automates this task by assigning image labels based on free-text expert radiology reports (Wood et al, 2020).…”