Publications
353 results found
Larrazabal AJ, Martínez C, Glocker B, et al., 2020, Post-DAE: anatomically plausible segmentation via post-processing with denoising autoencoders, IEEE Transactions on Medical Imaging, Vol: 39, Pages: 3813-3820, ISSN: 0278-0062
We introduce Post-DAE, a post-processing method based on denoisingautoencoders (DAE) to improve the anatomical plausibility of arbitrarybiomedical image segmentation algorithms. Some of the most popular segmentationmethods (e.g. based on convolutional neural networks or random forestclassifiers) incorporate additional post-processing steps to ensure that theresulting masks fulfill expected connectivity constraints. These methodsoperate under the hypothesis that contiguous pixels with similar aspect shouldbelong to the same class. Even if valid in general, this assumption does notconsider more complex priors like topological restrictions or convexity, whichcannot be easily incorporated into these methods. Post-DAE leverages the latestdevelopments in manifold learning via denoising autoencoders. First, we learn acompact and non-linear embedding that represents the space of anatomicallyplausible segmentations. Then, given a segmentation mask obtained with anarbitrary method, we reconstruct its anatomically plausible version byprojecting it onto the learnt manifold. The proposed method is trained usingunpaired segmentation mask, what makes it independent of intensity informationand image modality. We performed experiments in binary and multi-labelsegmentation of chest X-ray and cardiac magnetic resonance images. We show howerroneous and noisy segmentation masks can be improved using Post-DAE. Withalmost no additional computation cost, our method brings erroneoussegmentations back to a feasible space.
Oktay O, Nanavati J, Schwaighofer A, et al., 2020, Evaluation of deep learning to augment image-guided radiotherapy for head and neck and prostate cancers, Jama Network Open, Vol: 3, Pages: 1-11, ISSN: 2574-3805
Importance: Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions. Objective: To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy. Design, Setting, and Participants: This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020. Main Outcomes and Measures: The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch. Results: A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and inte
Popescu S, Sharp D, Cole J, et al., 2020, Hierarchical Gaussian processes with Wasserstein-2 kernels, Publisher: arXiv
We investigate the usefulness of Wasserstein-2 kernels in the context ofhierarchical Gaussian Processes. Stemming from an observation that stackingGaussian Processes severely diminishes the model's ability to detect outliers,which when combined with non-zero mean functions, further extrapolates lowvariance to regions with low training data density, we posit that directlytaking into account the variance in the computation of Wasserstein-2 kernels isof key importance towards maintaining outlier status as we progress through thehierarchy. We propose two new models operating in Wasserstein space which canbe seen as equivalents to Deep Kernel Learning and Deep GPs. Through extensiveexperiments, we show improved performance on large scale datasets and improvedout-of-distribution detection on both toy and real data.
Mitchell JR, Kamnitsas K, Singleton KW, et al., 2020, Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data, Journal of Medical Imaging, Vol: 7, Pages: 055501-1-055501-19, ISSN: 2329-4302
Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations.Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap).Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p < 0.00007). The DL method achieved a mean Dice coefficient of 0.87 on the test cases.Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its “human teachers” and produced output that was better, on average, than its training data.
McCouat J, Glocker B, 2020, Vertebrae detection and localization in CT with two-stage CNNs and dense annotations, Computational Methods and Clinical Applications in Musculoskeletal Imaging (MSKI), Pages: 1-10, ISSN: 0302-9743
We propose a new, two-stage approach to the vertebrae cen-troid detection and localization problem. The first stage detects wherethe vertebrae appear in the scan using 3D samples, the second identifiesthe specific vertebrae within that region-of-interest using 2D slices. Oursolution utilizes new techniques to improve the accuracy of the algorithmsuch as a revised approach to dense labelling from sparse centroid anno-tations and usage of large anisotropic kernels in the base level of a U-netarchitecture to maximize the receptive field. Our method improves thestate-of-the-art’s mean localization accuracy by 0.87mm on a publiclyavailable spine CT benchmark.
Grzech D, Kainz B, Glocker B, et al., 2020, Image registration via stochastic gradient markov chain monte carlo, Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Publisher: Springer International Publishing, Pages: 3-12, ISSN: 0302-9743
We develop a fully Bayesian framework for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images along with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backprop and the variational inference by backprop frameworks in order to efficiently draw thousands of samples from the posterior distribution. Regarding the modelling issues, we carefully design a Bayesian model for registration to overcome the existing barriers when using a dense, high-dimensional, and diffeomorphic parameterisation of the transformation. This results in improved calibration of uncertainty estimates.
Mathieu F, Güting H, Gravesteijn B, et al., 2020, Impact of Antithrombotic Agents on Radiological Lesion Progression in Acute Traumatic Brain Injury: A CENTER-TBI Propensity-Matched Cohort Analysis., J Neurotrauma, Vol: 37, Pages: 2069-2080
An increasing number of elderly patients are being affected by traumatic brain injury (TBI) and a significant proportion are on pre-hospital antithrombotic therapy for cardio- or cerebrovascular indications. We have quantified the impact of antiplatelet/anticoagulant (APAC) agents on radiological lesion progression in acute TBI, using a novel, semi-automated approach to volumetric lesion measurement, and explored the impact of use on clinical outcomes in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. We used a 1:1 propensity-matched cohort design, matching controls to APAC users based on demographics, baseline clinical status, pre-injury comorbidities, and injury severity. Subjects were selected from a pool of patients enrolled in CENTER-TBI with computed tomography (CT) scan at admission and repeated within 7 days of injury. We calculated absolute changes in volume of intraparenchymal, extra-axial, intraventricular, and total intracranial hemorrhage (ICH) between scans, and compared volume of hemorrhagic progression, proportion of patients with significant degree of progression (>25% of initial volume), proportion with new ICH on follow-up CT, as well as clinical course and outcomes. A total of 316 patients were included (158 APAC users; 158 controls). The mean volume of progression was significantly higher in the APAC group for extra-axial (3.1 vs. 1.3 mL, p = 0.01), but not intraparenchymal (3.8 vs. 4.6 mL, p = 0.65), intraventricular (0.2 vs. 0.0 mL, p = 0.79), or total intracranial hemorrhage (ICH; 7.0 vs. 6.0 mL, p = 0.08). More patients had significant hemorrhage growth (54.1 vs. 37.0%, p = 0.003) and delayed ICH (4 of 18 vs. none; p = 0.04) in the APAC group compared with controls, but this was not associated with differences in length of stay (LOS), rates of neurosurgical intervention
Pawlowski N, Castro DC, Glocker B, 2020, Deep structural causal models for tractable counterfactual inference, Neural Information Processing Systems (NeurIPS), Publisher: arXiv
We formulate a general framework for building structural causal models (SCMs)with deep learning components. The proposed approach employs normalising flowsand variational inference to enable tractable inference of exogenous noisevariables - a crucial step for counterfactual inference that is missing fromexisting deep causal learning methods. Our framework is validated on asynthetic dataset built on MNIST as well as on a real-world medical dataset ofbrain MRI scans. Our experimental results indicate that we can successfullytrain deep SCMs that are capable of all three levels of Pearl's ladder ofcausation: association, intervention, and counterfactuals, giving rise to apowerful new approach for answering causal questions in imaging applicationsand beyond. The code for all our experiments is available athttps://github.com/biomedia-mira/deepscm.
Ostberg A, Ledig C, Katila A, et al., 2020, Volume change in frontal cholinergic structures after traumatic brain injury and cognitive outcome, Frontiers in Neurology, Vol: 11, Pages: 1-10, ISSN: 1664-2295
The cholinergic nuclei in the basal forebrain innervate frontal cortical structures regulating attention. Our aim was to investigate if cognitive test results measuring attention relate to the longitudinal volume change of cholinergically innervated structures following traumatic brain injury (TBI). During the prospective, observational TBIcare project patients with all severities of TBI (n = 114) and controls with acute orthopedic injuries (n = 17) were recruited. Head MRI was obtained in both acute (mean 2 weeks post-injury) and late (mean 8 months) time points. T1-weighted 3D MR images were analyzed with an automatic segmentation method to evaluate longitudinal, structural brain volume change. The cognitive outcome was assessed with the Cambridge Neuropsychological Test Automated Battery (CANTAB). Analyses included 16 frontal cortical structures, of which four showed a significant correlation between post-traumatic volume change and the CANTAB test results. The strongest correlation was found between the volume loss of the supplementary motor cortex and motor screening task results (R-sq 0.16, p < 0.0001), where poorer test results correlated with greater atrophy. Of the measured sum structures, greater cortical gray matter atrophy rate showed a significant correlation with the poorer CANTAB test results. TBI caused volume loss of frontal cortical structures that are heavily innervated by cholinergic neurons is associated with neuropsychological test results measuring attention.
Coelho De Castro D, Walker I, Glocker B, 2020, Causality matters in medical imaging, Nature Communications, Vol: 11, Pages: 1-10, ISSN: 2041-1723
Causal reasoning can shed new light on the major challenges in ma-chine learning for medical imaging: scarcity of high-quality annotated data and mismatch between the development dataset and the target environment. A causal perspective on these issues allows decisions about data collection, annotation, preprocessing, and learning strategies to be made and scrutinized more transparently, while providing a detailed categorisation of potential biases and mitigation techniques. Along with worked clinical examples, we highlight the importance of establishing the causal relationship between images and their annotations, and offer step-by-step recommendations for future studies.
Zeiler FA, Mathieu F, Monteiro M, et al., 2020, Diffuse Intracranial Injury Patterns Are Associated with Impaired Cerebrovascular Reactivity in Adult Traumatic Brain Injury: A CENTER-TBI Validation Study, JOURNAL OF NEUROTRAUMA, Vol: 37, Pages: 1597-1608, ISSN: 0897-7151
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- Citations: 13
Popescu SG, Whittington A, Gunn RN, et al., 2020, Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease, Human Brain Mapping, Vol: 41, Pages: 4406-4418, ISSN: 1065-9471
Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid‐β42, total tau, phosphorylated tau), [18F]florbetapir, hippocampal volume and brain‐age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two‐way interaction models. The best performing model included an interaction between amyloid‐β‐PET and P‐tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker ‘space', providing information for per
Dou Q, Liu Q, Heng PA, et al., 2020, Unpaired multi-modal segmentation via knowledge distillation, IEEE Transactions on Medical Imaging, Vol: 39, Pages: 2415-2425, ISSN: 0278-0062
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model, we introduce a novel loss term inspired by knowledge distillation, by explicitly constraining the KL-divergence of our derived prediction distributions between modalities. We have extensively validated our approach on two multi-class segmentation problems: i) cardiac structure segmentation, and ii) abdominal organ segmentation. Different network settings, i.e., 2D dilated network and 3D U-net, are utilized to investigate our method's general efficacy. Experimental results on both tasks demonstrate that our novel multi-modal learning scheme consistently outperforms single-modal training and previous multi-modal approaches.
Folgoc LL, Baltatzis V, Alansary A, et al., 2020, Bayesian sampling bias correction: training with the right loss function, Publisher: arXiv
We derive a family of loss functions to train models in the presence ofsampling bias. Examples are when the prevalence of a pathology differs from itssampling rate in the training dataset, or when a machine learning practionerrebalances their training dataset. Sampling bias causes large discrepanciesbetween model performance in the lab and in more realistic settings. It isomnipresent in medical imaging applications, yet is often overlooked attraining time or addressed on an ad-hoc basis. Our approach is based onBayesian risk minimization. For arbitrary likelihood models we derive theassociated bias corrected loss for training, exhibiting a direct connection toinformation gain. The approach integrates seamlessly in the current paradigm of(deep) learning using stochastic backpropagation and naturally with Bayesianmodels. We illustrate the methodology on case studies of lung nodule malignancygrading.
Monteiro M, Newcombe VFJ, Mathieu F, et al., 2020, Multi-class semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning – an algorithm development and multi-centre validation study, The Lancet. Digital Health, Vol: 2, Pages: e314-e322, ISSN: 2589-7500
Background CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional userequires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognosticimportance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types.Methods Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained andvalidated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN wasused to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From thisdataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. Theperformance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification,lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation wasdone on an independent set of 500 patients from India.Findings 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres:184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derivedlesion volumes showed a mean difference of 0·86 mL (95% CI –5·23 to 6·94) for intraparenchymal haemorrhage,1·83 mL (–12·01 to 15·66) for extra-axial haemorrhage, 2·09 mL (–9·38 to 13·56) for perilesional oedema, and0·07 mL (–1·00 to 1·13) for intraventricular haemorrhage.Interpretation We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagiclesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification oflesion burden and progression, with potential applications for personalised treatment strategies
Vande Vyvere T, De La Rosa E, Wilms G, et al., 2020, Prognostic Validation of the NINDS Common Data Elements for the Radiologic Reporting of Acute Traumatic Brain Injuries: A CENTER-TBI Study., J Neurotrauma, Vol: 37, Pages: 1269-1282
The aim of this study is to investigate the prognostic value of using the National Institute of Neurological Disorders and Stroke (NINDS) standardized imaging-based pathoanatomic descriptors for the evaluation and reporting of acute traumatic brain injury (TBI) lesions. For a total of 3392 patients (2244 males and 1148 females, median age = 51 years) enrolled in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we extracted 96 Common Data Elements (CDEs) from the structured reports, spanning all three levels of pathoanatomic information (i.e., 20 "basic," 60 "descriptive," and 16 "advanced" CDE variables per patient). Six-month clinical outcome scores were dichotomized into favorable (Glasgow Outcome Scale Extended [GOS-E] = 5-8) versus unfavorable (GOS-E = 1-4). Regularized logistic regression models were constructed and compared using the optimism-corrected area under the curve (AUC). An abnormality was reported for the majority of patients (64.51%). In 79.11% of those patients, there was at least one coexisting pathoanatomic lesion or associated finding. An increase in lesion severity, laterality, and volume was associated with more unfavorable outcomes. Compared with the full set of pathoanatomic descriptors (i.e., all three categories of information), reporting "basic" CDE information provides at least equal discrimination between patients with favorable versus unfavorable outcome (AUC = 0.8121 vs. 0.8155, respectively). Addition of a selected subset of "descriptive" detail to the basic CDEs could improve outcome prediction (AUC = 0.8248). Addition of "advanced" or "emerging/exploratory" information had minimal prognostic value. Our results show that the NINDS standardized-imaging based pathoanatomic descriptors can be used in large-scale studies and provide importan
Monteiro M, Kamnitsas K, Ferrante E, et al., 2020, TBI lesion segmentation in head CT: impact of preprocessing and data augmentation, MICCAI Brain Lesion Workshop, Publisher: Springer Verlag, Pages: 13-22, ISSN: 0302-9743
Automatic segmentation of lesions in head CT provides keyinformation for patient management, prognosis and disease monitoring.Despite its clinical importance, method development has mostly focusedon multi-parametric MRI. Analysis of the brain in CT is challengingdue to limited soft tissue contrast and its mono-modal nature. We studythe under-explored problem of fine-grained CT segmentation of multiplelesion types (core, blood, oedema) in traumatic brain injury (TBI). Weobserve that preprocessing and data augmentation choices greatly impactthe segmentation accuracy of a neural network, yet these factors arerarely thoroughly assessed in prior work. We design an empirical studythat extensively evaluates the impact of different data preprocessing andaugmentation methods. We show that these choices can have an impactof up to 18% DSC. We conclude that resampling to isotropic resolutionyields improved performance, skull-stripping can be replaced by using theright intensity window, and affine-to-atlas registration is not necessaryif we use sufficient spatial augmentation. Since both skull-stripping andaffine-to-atlas registration are susceptible to failure, we recommend theiralternatives to be used in practice. We believe this is the first work toreport results for fine-grained multi-class segmentation of TBI in CT. Ourfindings may inform further research in this under-explored yet clinicallyimportant task of automatic head CT lesion segmentation.
Serruys PW, Chichareon P, Modolo R, et al., 2020, The SYNTAX score on its way out or ... towards artificial intelligence: part II, EUROINTERVENTION, Vol: 16, Pages: 60-75, ISSN: 1774-024X
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- Citations: 11
Serruys PW, Chichareon P, Modolo R, et al., 2020, The SYNTAX score on its way out or ... towards artificial intelligence: part I, EUROINTERVENTION, Vol: 16, Pages: 44-59, ISSN: 1774-024X
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- Citations: 18
Monteiro M, Glocker B, 2020, biomedia-mira/blast-ct: v0.1.1
Brain Lesion Analysis and Segmentation Tool for Computed Tomography
Mathieu F, Zeiler FA, Ercole A, et al., 2020, Relationship between measures of cerebrovascular reactivity and intracranial lesion progression in acute TBI patients: a CENTER-TBI study, Journal of Neurotrauma, ISSN: 0897-7151
Tarroni G, Bai W, Oktay O, et al., 2020, Large-scale quality control of cardiac imaging in population studies: application to UK Biobank, Scientific Reports, Vol: 10, ISSN: 2045-2322
In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment isunfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) imagesto the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics(heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factorsincluding acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage(i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of thestacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slicemotion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastoliccardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involvedin UKBB CMR acquisition and for the ones who use the dataset for research purposes.
Jimenez-Pastor A, Alberich-Bayarri A, Fos-Guarinos B, et al., 2020, Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data, RADIOLOGIA MEDICA, Vol: 125, Pages: 48-56, ISSN: 0033-8362
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- Citations: 10
Matzkin F, Newcombe VFJ, Stevenson S, et al., 2020, Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy., Publisher: Springer, Pages: 390-399
Robinson R, Dou Q, Castro DCD, et al., 2020, Image-Level Harmonization of Multi-site Data Using Image-and-Spatial Transformer Networks., Publisher: Springer, Pages: 710-719
Matzkin F, Newcombe VFJ, Glocker B, et al., 2020, Cranial Implant Design via Virtual Craniectomy with Shape Priors., Publisher: Springer, Pages: 37-46
Wang S, Tarroni G, Qin C, et al., 2020, Deep Generative Model-Based Quality Control for Cardiac MRI Segmentation., Publisher: Springer, Pages: 88-97
Monteiro M, Le Folgoc L, Coelho de Castro D, et al., 2020, Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty, Publisher: Curran Associates, Inc., Pages: 12756-12767
Glocker B, Robinson R, Castro DC, et al., 2019, Machine learning with multi-site imaging data: an empirical study on theimpact of scanner effects, Medical Imaging meets NeurIPS, Publisher: NeurIPS
This is an empirical study to investigate the impact of scanner effects when us-ing machine learning on multi-site neuroimaging data. We utilize structural T1-weighted brain MRI obtained from two different studies, Cam-CAN and UKBiobank. For the purpose of our investigation, we construct a dataset consisting ofbrain scans from 592 age- and sex-matched individuals, 296 subjects from eachoriginal study. Our results demonstrate that even after careful pre-processing withstate-of-the-art neuroimaging pipelines a classifier can easily distinguish betweenthe origin of the data with very high accuracy. Our analysis on the example appli-cation of sex classification suggests that current approaches to harmonize data areunable to remove scanner-specific bias leading to overly optimistic performanceestimates and poor generalization. We conclude that multi-site data harmonizationremains an open challenge and particular care needs to be taken when using suchdata with advanced machine learning methods for predictive modelling.
Dou Q, Coelho De Castro D, Kamnitsas K, et al., 2019, Domain generalization via model-agnostic learning of semantic features, Neural Information Processing Systems (NeurIPS), Publisher: Neural Information Processing Systems Foundation, Inc., ISSN: 1049-5258
Generalization capability to unseen domains is crucial for machine learning modelswhen deploying to real-world conditions. We investigate the challenging problemof domain generalization, i.e., training a model on multi-domain source data suchthat it can directly generalize to target domains with unknown statistics. We adopta model-agnostic learning paradigm with gradient-based meta-train and meta-testprocedures to expose the optimization to domain shift. Further, we introducetwo complementary losses which explicitly regularize the semantic structure ofthe feature space. Globally, we align a derived soft confusion matrix to preservegeneral knowledge about inter-class relationships. Locally, we promote domain-independent class-specific cohesion and separation of sample features with ametric-learning component. The effectiveness of our method is demonstrated withnew state-of-the-art results on two common object recognition benchmarks. Ourmethod also shows consistent improvement on a medical image segmentation task.
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