Publications
173 results found
Day TG, Simpson JM, Razavi R, et al., 2023, Improving image labelling quality, Nature Machine Intelligence, Vol: 5, Pages: 335-336
Vlontzos A, Kainz B, Gilligan-Lee CM, 2023, Estimating categorical counterfactuals via deep twin networks, Nature Machine Intelligence, Vol: 5, Pages: 159-168, ISSN: 2522-5839
Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, we require knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that the resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but for the case of categorical variables, it remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle positing desirable properties that causal mechanisms should possess and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference—an alternative to the abduction–action–prediction method. We empirically test our approach on diverse real-world and semisynthetic data from medicine, epidemiology and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced.
Ma Q, Li L, Robinson EC, et al., 2023, CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 42, Pages: 430-443, ISSN: 0278-0062
Schmidtke L, Hou B, Vlontzos A, et al., 2023, Self-supervised 3D Human Pose Estimation in Static Video via Neural Rendering, Pages: 704-713, ISSN: 0302-9743
Inferring 3D human pose from 2D images is a challenging and long-standing problem in the field of computer vision with many applications including motion capture, virtual reality, surveillance or gait analysis for sports and medicine. We present preliminary results for a method to estimate 3D pose from 2D video containing a single person and a static background without the need for any manual landmark annotations. We achieve this by formulating a simple yet effective self-supervision task: our model is required to reconstruct a random frame of a video given a frame from another timepoint and a rendered image of a transformed human shape template. Crucially for optimisation, our ray casting based rendering pipeline is fully differentiable, enabling end to end training solely based on the reconstruction task.
Zimmer VA, Gomez A, Skelton E, et al., 2023, Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view, Medical Image Analysis, Vol: 83, ISSN: 1361-8415
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.
Zimmerer D, Full PM, Isensee F, et al., 2022, MOOD 2020: A public benchmark for out-of-distribution detection and localization on medical images, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 2728-2738, ISSN: 0278-0062
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development.
Vlontzos A, Kainz B, Lee C, 2022, Estimating Categorical Counterfactuals via Deep Twin Networks
<jats:title>Abstract</jats:title> <jats:p>Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but the case of categorical variables remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle that posits desirable properties causal mechanisms should posses, and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference---an alternative to the abduction, action, & prediction method. We empirically test our approach on diverse real-world and semi-synthetic data from medicine, epidemiology, and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced.</jats:p>
Grzech D, Azampour MF, Qiu H, et al., 2022, Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo, Publisher: ArXiv
We develop a new Bayesian model for non-rigid registration ofthree-dimensional medical images, with a focus on uncertainty quantification.Probabilistic registration of large images with calibrated uncertaintyestimates is difficult for both computational and modelling reasons. To addressthe computational issues, we explore connections between the Markov chain MonteCarlo by backpropagation and the variational inference by backpropagationframeworks, in order to efficiently draw samples from the posteriordistribution of transformation parameters. To address the modelling issues, weformulate a Bayesian model for image registration that overcomes the existingbarriers when using a dense, high-dimensional, and diffeomorphic transformationparametrisation. This results in improved calibration of uncertainty estimates.We compare the model in terms of both image registration accuracy anduncertainty quantification to VoxelMorph, a state-of-the-art image registrationmodel based on deep learning.
Dou Q, So TY, Jiang M, et al., 2022, Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study, npj Digital Medicine, Vol: 5, ISSN: 2398-6352
Correction to: npj Digital Medicine https://doi.org/10.1038/s41746-021-00431-6, published online 29 March 2021
Tan J, Hou B, Batten J, et al., 2022, Detecting outliers with foreign patch interpolation, Journal of Machine Learning for Biomedical Imaging, Vol: 2022, Pages: 1-27, ISSN: 2766-905X
In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model.Our code is available at https://github.com/jemtan/FPI
Tan J, Kart T, Hou B, et al., 2022, MetaDetector: Detecting outliers by learning to learn from self-supervision, Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, Publisher: Springer, Pages: 119-126, ISSN: 0302-9743
Using self-supervision in anomaly detection can increase sensitivity to subtle irregularities. However, increasing sensitivity to certain classes of outliers could result in decreased sensitivity to other types. While a single model may have limited coverage, an adaptive method could help detect a broader range of outliers. Our proposed method explores whether meta learning can increase the adaptability of self-supervised methods. Meta learning is often employed in few-shot settings with labelled examples. To use it for anomaly detection, where labelled support data is usually not available, we instead construct a self-supervised task using the test input itself and reference samples from the normal training data. Specifically, patches from the test image are introduced into normal reference images. This forms the basis of the few-shot task. During training, the same few-shot process is used, but the test/query image is substituted with a normal training image that contains a synthetic irregularity. Meta learning is then used to learn how to learn from the few-shot task by computing second order gradients. Given the importance of screening applications, e.g. in healthcare or security, any adaptability in the method must be counterbalanced with robustness. As such, we add strong regularization by i) restricting meta learning to only layers near the bottleneck of our encoder-decoder architecture and ii) computing the loss at multiple points during the few-shot process.
Liu T, Meng Q, Huang J-J, et al., 2022, Video summarization through reinforcement learning with a 3D spatio-temporal U-Net, IEEE Transactions on Image Processing, Vol: 31, Pages: 1573-1586, ISSN: 1057-7149
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames. In this paper, we introduce the 3DST-UNet-RL framework for video summarization. A 3D spatio-temporal U-Net is used to efficiently encode spatio-temporal information of the input videos for downstream reinforcement learning (RL). An RL agent learns from spatio-temporal latent scores and predicts actions for keeping or rejecting a video frame in a video summary. We investigate if real/inflated 3D spatio-temporal CNN features are better suited to learn representations from videos than commonly used 2D image features. Our framework can operate in both, a fully unsupervised mode and a supervised training mode. We analyse the impact of prescribed summary lengths and show experimental evidence for the effectiveness of 3DST-UNet-RL on two commonly used general video summarization benchmarks. We also applied our method on a medical video summarization task. The proposed video summarization method has the potential to save storage costs of ultrasound screening videos as well as to increase efficiency when browsing patient video data during retrospective analysis or audit without loosing essential information.
Grzech D, Azampour MF, Glocker B, et al., 2022, A variational Bayesian method for similarity learning in non-rigid image registration, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE COMPUTER SOC, Pages: 119-128, ISSN: 1063-6919
Schluter HM, Tan J, Hou B, et al., 2022, Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization, Editors: Avidan, Brostow, Cisse, Farinella, Hassner, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 474-489, ISBN: 978-3-031-19820-5
Reynaud H, Vlontzos A, Dombrowski M, et al., 2022, D'ARTAGNAN: Counterfactual Video Generation, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 599-609, ISSN: 0302-9743
Baugh M, Tan J, Vlontzos A, et al., 2022, nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods, Editors: Sudre, Baumgartner, Dalca, Qin, Tanno, VanLeemput, Wells, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 103-112, ISBN: 978-3-031-16748-5
Ouyang C, Wang S, Chen C, et al., 2022, Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation, 4th International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 59-69, ISSN: 0302-9743
Lebbos C, Barcroft J, Tan J, et al., 2022, Adnexal Mass Segmentation with Ultrasound Data Synthesis, Editors: Aylward, Noble, Hu, Lee, Baum, Min, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 106-116, ISBN: 978-3-031-16901-4
Li L, Ma Q, Li Z, et al., 2022, Fetal Cortex Segmentation with Topology and Thickness Loss Constraints, 1st Workshop on Ethical and Philosop Issues in Med Imaging (EPIMI) / 12th Int Workshop on Multimodal Learning and Fus Across Scales for Clin Decis Support (ML-CDS) / 2nd Int Workshop on Topol Data Anal for Biomed Imaging (TDA4BiomedicalImaging), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 123-133, ISSN: 0302-9743
Gomez A, Zimmer VA, Wheeler G, et al., 2022, PRETUS: A plug-in based platform for real-time ultrasound imaging research, SoftwareX, Vol: 17, ISSN: 2352-7110
We present PRETUS — a Plugin-based Real Time UltraSound software platform for live ultrasound image analysis and operator support. The software is lightweight; functionality is brought in via independent plug-ins that can be arranged in sequence. The software allows to capture the real-time stream of ultrasound images from virtually any ultrasound machine, applies computational methods and visualizes the results on-the-fly.Plug-ins can run concurrently without blocking each other. They can be implemented in C++ and Python. A graphical user interface can be implemented for each plug-in, and presented to the user in a compact way. The software is free and open source, and allows for rapid prototyping and testing of real-time ultrasound imaging methods in a manufacturer-agnostic fashion. The software is provided with input, output and processing plug-ins, as well as with tutorials to illustrate how to develop new plug-ins for PRETUS.
Schmidtke L, Vlontzos A, Ellershaw S, et al., 2021, Unsupervised human pose estimation through transforming shape templates, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE Computer Society, Pages: 2484-2494, ISSN: 1063-6919
Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to gen-eralize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants. Project page: infantmotion.github.io
Matthew J, Skelton E, Day TG, et al., 2021, Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time, Prenatal Diagnosis, Vol: 42, Pages: 49-59, ISSN: 0197-3851
ObjectiveAdvances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools.MethodsA prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning.ResultsTwenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI-assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks.ConclusionSeparating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.
Budd S, Patkee P, Baburamani A, et al., 2021, Surface agnostic metrics for cortical volume segmentation and regression, The 3rd Workshop on Machine Learning in Clinical Neuroimaging, Publisher: Springer, Pages: 3-12, ISSN: 0302-9743
The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.
Budd S, Sinclair M, Day T, et al., 2021, Detecting hypo-plastic left heart syndrome in fetal ultrasound via disease-specific atlas maps, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer, Pages: 207-217, ISSN: 0302-9743
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single ‘4 Chamber Heart’ view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing).
Reynaud H, Vlontzos A, Hou B, et al., 2021, Ultrasound video transformers for cardiac ejection fraction estimation, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer, Pages: 495-505, ISSN: 0302-9743
Cardiac ultrasound imaging is used to diagnose various heart diseases. Common analysis pipelines involve manual processing of the video frames by expert clinicians. This suffers from intra- and inter-observer variability. We propose a novel approach to ultrasound video analysis using a transformer architecture based on a Residual Auto-Encoder Network and a BERT model adapted for token classification. This enables videos of any length to be processed. We apply our model to the task of End-Systolic (ES) and End-Diastolic (ED) frame detection and the automated computation of the left ventricular ejection fraction. We achieve an average frame distance of 3.36 frames for the ES and 7.17 frames for the ED on videos of arbitrary length. Our end-to-end learnable approach can estimate the ejection fraction with a MAE of 5.95 and R2 of 0.52 in 0.15 s per video, showing that segmentation is not the only way to predict ejection fraction. Code and models are available at https://github.com/HReynaud/UVT.
Hou B, Kaissis G, Summers RM, et al., 2021, RATCHET: Medical transformer for chest X-ray diagnosis and reporting, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer, Pages: 293-303, ISSN: 0302-9743
Chest radiographs are one of the most common diagnostic modalities in clinical routine. It can be done cheaply, requires minimal equipment, and the image can be diagnosed by every radiologists. However, the number of chest radiographs obtained on a daily basis can easily overwhelm the available clinical capacities. We propose RATCHET: RAdiological Text Captioning for Human Examined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is trained end-to-end. It is capable of extracting image features from chest radiographs, and generates medically accurate text reports that fit seamlessly into clinical work flows. The model is evaluated for its natural language generation ability using common metrics from NLP literature, as well as its medically accuracy through a surrogate report classification task. The model is available for download at: http://www.github.com/farrell236/RATCHET.
Ma Q, Robinson EC, Kainz B, et al., 2021, PialNN: A fast deep learning framework for cortical pial surface reconstruction, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer, Pages: 73-81, ISSN: 0302-9743
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches. The codes are publicly available at https://github.com/m-qiang/PialNN.
Tan J, Hou B, Day T, et al., 2021, Detecting outliers with poisson image interpolation, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer, Pages: 581-591, ISSN: 0302-9743
Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearance from only healthy data have shown promising results recently. We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection. Our approach originates in the foreign patch interpolation (FPI) strategy that has shown superior performance on brain MRI and abdominal CT data. We propose to use a better patch interpolation strategy, Poisson image interpolation (PII), which makes our method suitable for applications in challenging data regimes. PII outperforms state-of-the-art methods by a good margin when tested on surrogate tasks like identifying common lung anomalies in chest X-rays or hypo-plastic left heart syndrome in prenatal, fetal cardiac ultrasound images. Code available at https://github.com/jemtan/PII.
Li L, Sinclair M, Makropoulos A, et al., 2021, CAS-Net: Conditional atlas generation and brain segmentation for fetal MRI, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer, Pages: 221-230, ISSN: 0302-9743
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The conditional atlases provide anatomical priors that can constrain the segmentation connectivity, despite the heterogeneity of intensity values caused by motion or partial volume effects. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project (dHCP). The results demonstrate that the proposed method can generate conditional age-specific atlas with sharp boundary and shape variance. It also segment multi-category brain tissues for fetal MRI with a high overall Dice similarity coefficient (DSC) of 85.2% for the selected 9 tissue labels.
Chartsias A, Gao S, Mumith A, et al., 2021, Contrastive learning for view classification of echocardiograms, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer, Pages: 149-158, ISSN: 0302-9743
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image features. However, such models are extremely data-hungry and training requires labelling of many thousands of images by experienced clinicians. Here we propose the use of contrastive learning to mitigate the labelling bottleneck. We train view classification models for imbalanced cardiac ultrasound datasets and show improved performance for views/classes for which minimal labelled data is available. Compared to a naïve baseline model, we achieve an improvement in F1 score of up to 26% in those views while maintaining state-of-the-art performance for the views with sufficiently many labelled training observations.
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