7 research outputs found
Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation
Training neural networks using limited annotations is an important problem in the medical domain. Deep Neural Networks (DNNs) typically require large, annotated datasets to achieve acceptable performance which, in the medical domain, are especially difficult to obtain as they require significant time from expert radiologists. Semi-supervised learning aims to overcome this problem by learning segmentations with very little annotated data, whilst exploiting large amounts of unlabelled data. However, the best-known technique, which utilises inferred pseudo-labels, is vulnerable to inaccurate pseudo-labels degrading the performance. We propose a framework based on superpixels - meaningful clusters of adjacent pixels - to improve the accuracy of the pseudo labels and address this issue. Our framework combines superpixels with semi-supervised learning, refining the pseudo-labels during training using the features and edges of the superpixel maps. This method is evaluated on a multimodal magnetic resonance imaging (MRI) dataset for the task of brain tumour region segmentation. Our method demonstrates improved performance over the standard semi-supervised pseudo-labelling baseline when there is a reduced annotator burden and only 5 annotated patients are available. We report DSC=0.824 and DSC=0.707 for the test set whole tumour and tumour core regions respectively
Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation
Training neural networks using limited annotations is an important problem in the medical domain. Deep Neural Networks (DNNs) typically require large, annotated datasets to achieve acceptable performance which, in the medical domain, are especially difficult to obtain as they require significant time from expert radiologists. Semi-supervised learning aims to overcome this problem by learning segmentations with very little annotated data, whilst exploiting large amounts of unlabelled data. However, the best-known technique, which utilises inferred pseudo-labels, is vulnerable to inaccurate pseudo-labels degrading the performance. We propose a framework based on superpixels - meaningful clusters of adjacent pixels - to improve the accuracy of the pseudo labels and address this issue. Our framework combines superpixels with semi-supervised learning, refining the pseudo-labels during training using the features and edges of the superpixel maps. This method is evaluated on a multimodal magnetic resonance imaging (MRI) dataset for the task of brain tumour region segmentation. Our method demonstrates improved performance over the standard semi-supervised pseudo-labelling baseline when there is a reduced annotator burden and only 5 annotated patients are available. We report DSC=0.824 and DSC=0.707 for the test set whole tumour and tumour core regions respectively
The role of noise in denoising models for anomaly detection in medical images
Pathological brain lesions exhibit diverse appearance in brain images, in
terms of intensity, texture, shape, size, and location. Comprehensive sets of
data and annotations are difficult to acquire. Therefore, unsupervised anomaly
detection approaches have been proposed using only normal data for training,
with the aim of detecting outlier anomalous voxels at test time. Denoising
methods, for instance classical denoising autoencoders (DAEs) and more recently
emerging diffusion models, are a promising approach, however naive application
of pixelwise noise leads to poor anomaly detection performance. We show that
optimization of the spatial resolution and magnitude of the noise improves the
performance of different model training regimes, with similar noise parameter
adjustments giving good performance for both DAEs and diffusion models. Visual
inspection of the reconstructions suggests that the training noise influences
the trade-off between the extent of the detail that is reconstructed and the
extent of erasure of anomalies, both of which contribute to better anomaly
detection performance. We validate our findings on two real-world datasets
(tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain
CT), showing good detection on diverse anomaly appearances. Overall, we find
that a DAE trained with coarse noise is a fast and simple method that gives
state-of-the-art accuracy. Diffusion models applied to anomaly detection are as
yet in their infancy and provide a promising avenue for further research.Comment: Submitted to Medical Image Analysis special issue for MIDL 202
Causal Machine Learning for Healthcare and Precision Medicine
Causal machine learning (CML) has experienced increasing popularity in
healthcare. Beyond the inherent capabilities of adding domain knowledge into
learning systems, CML provides a complete toolset for investigating how a
system would react to an intervention (e.g.\ outcome given a treatment).
Quantifying effects of interventions allows actionable decisions to be made
whilst maintaining robustness in the presence of confounders. Here, we explore
how causal inference can be incorporated into different aspects of clinical
decision support (CDS) systems by using recent advances in machine learning.
Throughout this paper, we use Alzheimer's disease (AD) to create examples for
illustrating how CML can be advantageous in clinical scenarios. Furthermore, we
discuss important challenges present in healthcare applications such as
processing high-dimensional and unstructured data, generalisation to
out-of-distribution samples, and temporal relationships, that despite the great
effort from the research community remain to be solved. Finally, we review
lines of research within causal representation learning, causal discovery and
causal reasoning which offer the potential towards addressing the
aforementioned challenges.Comment: 19 pages, 4 figures, 1 tabl
Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria
Background: In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy. Methods: In a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen’s kappa. Survival was assessed using Kaplan-Meier methodology. Results: Human and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm3 (p=0.182), 95% limits 345 to +407 cm3). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival. Conclusion: We have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies