Publications
1021 results found
Meissen F, Wiestler B, Kaissis G, et al., 2022, On the Pitfalls of Using the Residual Error as Anomaly Score, Pages: 914-928
Many current state-of-the-art methods for anomaly localization in medical images rely on calculating a residual image between a potentially anomalous input image and its (“healthy”) reconstruction. As the reconstruction of the unseen anomalous region should be erroneous, this yields large residuals as a score to detect anomalies in medical images. However, this assumption does not take into account residuals resulting from imperfect reconstructions of the machine learning models used. Such errors can easily overshadow residuals of interest and therefore strongly question the use of residual images as scoring function. Our work explores this fundamental problem of residual images in detail. We theoretically define the problem and thoroughly evaluate the influence of intensity and texture of anomalies against the effect of imperfect reconstructions in a series of experiments. Code and experiments are available under https://github.com/FeliMe/residual-score-pitfalls.
Ziller A, Passerat-Palmbach J, Trask A, et al., 2022, Artificial Intelligence in Medicine and Privacy Preservation, Artificial Intelligence in Medicine, Pages: 145-158, ISBN: 9783030645724
The widespread applicability of medical artificial intelligence systems hinges on their development and validation on large, diverse, and representative datasets. So far, such datasets have only been able to be assembled through multi-institutional data sharing and aggregation. Such practices are however associated with legal, ethical, and technical challenges and scale poorly to multinational efforts. They furthermore potentially infringe on data ownership and complicate the enforcement of data governance measures. Privacy-preserving machine learning offers solutions to these challenges by implementing techniques for the decentralized training of algorithms on datasets without requiring direct access to the data or by offering guarantees of privacy protection during training and algorithm inference. This chapter presents the core techniques of secure and private artificial intelligence, which can serve to enable the training of algorithms on larger datasets and their provision to more people under provable assurances of privacy and ownership protection.
Van Praag DLG, Wouters K, Van Den Eede F, et al., 2022, Neurocognitive correlates of probable posttraumatic stress disorder following traumatic brain injury, Brain and Spine, Vol: 2
Introduction: Neurocognitive problems associated with posttraumatic stress disorder (PTSD) can interact with impairment resulting from traumatic brain injury (TBI). Research question: We aimed to identify neurocognitive problems associated with probable PTSD following TBI in a civilian sample. Material and methods: The study is part of the CENTER-TBI project (Collaborative European Neurotrauma Effectiveness Research) that aims to better characterize TBI. For this cross-sectional study, we included patients of all severities aged over 15, and a Glasgow Outcome Score Extended (GOSE) above 3. Participants were assessed at six months post-injury on the PTSD Checklist-5 (PCL-5), the Trail Making Test (TMT), the Rey Auditory Verbal Learning Test (RAVLT) and the Cambridge Neuropsychological Test Automated Battery (CANTAB). Primary analysis was a complete case analysis. Regression analyses were performed to investigate the association between the PCL-5 and cognition. Results: Of the 1134 participants included in the complete case analysis, 13.5% screened positive for PTSD. Probable PTSD was significantly associated with higher TMT-(B-A) (OR = 1.35, 95% CI: 1.14–1.60, p < .001) and lower RAVLT-delayed recall scores (OR = 0.74, 95% CI: 0.61–0.91, p = .004) after controlling for age, sex, psychiatric history, baseline Glasgow Coma Scale and education. Discussion and conclusion: Poorer performance on cognitive tests assessing task switching and, to a lesser extent, delayed verbal recall is associated with probable PTSD in civilians who have suffered TBI.
Usynin D, Klause H, Paetzold JC, et al., 2022, Can Collaborative Learning Be Private, Robust and Scalable?, Pages: 37-46, ISSN: 0302-9743
In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the private data used by the federation or the integrity of the model itself. This requires the medical imaging community to develop mechanisms to train collaborative models that are private and robust against adversarial data. In response to these challenges, we propose a practical open-source framework to study the effectiveness of combining differential privacy, model compression and adversarial training to improve the robustness of models against adversarial samples under train- and inference-time attacks. Using our framework, we achieve competitive model performance, a significant reduction in model’s size and an improved empirical adversarial robustness without a severe performance degradation, critical in medical image analysis.
Marcus A, Bentley P, Rueckert D, 2022, Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-Based Network, Pages: 52-62, ISBN: 9783031178986
The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an event. These tasks are particularly challenging due to the subtle expression of acute ischemic lesions and their dynamic nature. Automation efforts have not yet applied deep learning to estimate lesion age and treated these two tasks independently, so, have overlooked their inherent complementary relationship. To leverage this, we propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. By utilizing gated positional self-attention and CT-specific data augmentation, our method can capture long-range spatial dependencies while maintaining its ability to be trained from scratch under low-data regimes commonly found in medical imaging. Further, to better combine multiple predictions, we incorporate uncertainty by utilizing quantile loss to facilitate estimating a probability density function of lesion age. The effectiveness of our model is then extensively evaluated on a clinical dataset consisting of 776 CT images from two medical centers. Experimental results demonstrate that our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages ≤ 4.5 h compared to 0.858 using a conventional approach, and outperforms task-specific state-of-the-art algorithms.
Jia X, Thorley A, Chen W, et al., 2022, Learning a Model-Driven Variational Network for Deformable Image Registration, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 41, Pages: 199-212, ISSN: 0278-0062
- Author Web Link
- Cite
- Citations: 7
Zou C, Mueller A, Wolfgang U, et al., 2022, Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label, IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE, Vol: 10, ISSN: 2168-2372
Menten MJ, Paetzold JC, Dima A, et al., 2022, Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs, MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VIII, Vol: 13438, Pages: 330-340, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 2
Tanzer M, Ferreira P, Scott A, et al., 2022, Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction, MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, MIUA 2022, Vol: 13413, Pages: 101-115, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Tanzer M, Yook SH, Ferreira P, et al., 2022, Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal, STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: REGULAR AND CMRXMOTION CHALLENGE PAPERS, STACOM 2022, Vol: 13593, Pages: 123-132, ISSN: 0302-9743
Kuestner T, Pan J, Gilliam C, et al., 2022, Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk, APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, Vol: 11, ISSN: 2048-7703
- Author Web Link
- Cite
- Citations: 1
Sideri-Lampretsa V, Kaissis G, Rueckert D, 2022, MULTI-MODAL UNSUPERVISED BRAIN IMAGE REGISTRATION USING EDGE MAPS, 19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI), Publisher: IEEE, ISSN: 1945-7928
- Author Web Link
- Cite
- Citations: 1
Meissen F, Kaissis G, Rueckert D, 2022, AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation, 24th Int Conf on Med Image Comp and Comp Assisted Intervent (MICCAI) / Conf on Mitosis Domain Generalizat Challenge (MIDOG) / Conf on Med Out-of-Distribut Analysis Challenge (MOOD) / Conf on Learn2Reg (L2R), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 127-135, ISSN: 0302-9743
Mueller P, Kaissis G, Zou C, et al., 2022, Joint Learning of Localized Representations from Medical Images and Reports, COMPUTER VISION, ECCV 2022, PT XXVI, Vol: 13686, Pages: 685-701, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Qiu H, Hammernik K, Qin C, et al., 2022, Embedding Gradient-Based Optimization in Image Registration Networks, MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI, Vol: 13436, Pages: 56-65, ISSN: 0302-9743
Muffoletto M, Xu H, Barbaroux H, et al., 2022, Comparison of Semi- and Un-Supervised Domain Adaptation Methods for Whole-Heart Segmentation, 13th International Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 91-100, ISSN: 0302-9743
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
Meng Q, Bai W, Liu T, et al., 2022, Mesh-Based 3D Motion Tracking in Cardiac MRI Using Deep Learning, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 248-258, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Mueller P, Kaissis G, Zou C, et al., 2022, Radiological Reports Improve Pre-training for Localized Imaging Tasks on Chest X-Rays, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 647-657, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Qiao M, Basaran BD, Qiu H, et al., 2022, Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data, 13th International Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 3-12, ISSN: 0302-9743
Binzer M, Hammernik K, Rueckert D, et al., 2022, Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data, 5th International Workshop on Predictive Intelligence in Medicine (PRIME MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 137-148, ISSN: 0302-9743
Meissen F, Kaissis G, Rueckert D, 2022, Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI, 7th International Brain Lesion Workshop (BrainLes), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 63-74, ISSN: 0302-9743
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
Pan J, Rueckert D, Kuestner T, et al., 2022, Learning-Based and Unrolled Motion-Compensated Reconstruction for Cardiac MR CINE Imaging, MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI, Vol: 13436, Pages: 686-696, ISSN: 0302-9743
Zolotareva O, Nasirigerdeh R, Matschinske J, et al., 2021, Flimma: a federated and privacy-aware tool for differential gene expression analysis, Genome Biology, Vol: 22, ISSN: 1474-7596
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.
Qin C, Duan J, Hammernik K, et al., 2021, Complementary time-frequency domain networks for dynamic parallel MR image reconstruction, MAGNETIC RESONANCE IN MEDICINE, Vol: 86, Pages: 3274-3291, ISSN: 0740-3194
Henriksen P, Hammernik K, Rueckert D, et al., 2021, Bias Field Robustness Verification of Large Neural Image Classifiers, British Machine Vision Conference (BMVC21)
Dimitrova R, Pietsch M, Ciarrusta J, et al., 2021, Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age, NeuroImage, Vol: 243, ISSN: 1053-8119
INTRODUCTION: The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS: We sought to first map typical cortical micro and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n=259). Then we offer a comprehensive characterisation of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS: In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION: We showed that preterm birth alters cortical micro and macrostructural maturation near
Eyre M, Fitzgibbon SP, Ciarrusta J, et al., 2021, The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity (vol 144, awab118, 2021), BRAIN, Vol: 144, ISSN: 0006-8950
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.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.