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Article ; Online: Privacy

Alexander Ziller / Tamara T. Mueller / Rickmer Braren / Daniel Rueckert / Georgios Kaissis

Entropy, Vol 24, Iss 714, p

An Axiomatic Approach

2022 Volume 714

Abstract: The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what ... ...

Abstract	The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what exactly is being protected, that is, of privacy itself. In this work, we formulate an axiomatic definition of privacy based on quantifiable and irreducible information flows. Our definition synthesizes prior work from the domain of social science with a contemporary understanding of PETs such as differential privacy (DP). Our work highlights the fact that the inevitable difficulties of protecting privacy in practice are fundamentally information-theoretic. Moreover, it enables quantitative reasoning about PETs based on what they are protecting, thus fostering objective policy discourse about their societal implementation.
Keywords	privacy ; information flow ; differential privacy ; confidentiality ; secrecy ; privacy-enhancing technologies ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
Subject code	303 ; 005
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Medical imaging deep learning with differential privacy

Alexander Ziller / Dmitrii Usynin / Rickmer Braren / Marcus Makowski / Daniel Rueckert / Georgios Kaissis

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 8

Abstract: Abstract The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations ... ...

Abstract	Abstract The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ethical requirements of the medical profession. Differential privacy (DP) enables the provision of information-theoretic privacy guarantees to patients and can be implemented in the setting of deep neural network training through the differentially private stochastic gradient descent (DP-SGD) algorithm. We here present deepee, a free-and-open-source framework for differentially private deep learning for use with the PyTorch deep learning framework. Our framework is based on parallelised execution of neural network operations to obtain and modify the per-sample gradients. The process is efficiently abstracted via a data structure maintaining shared memory references to neural network weights to maintain memory efficiency. We furthermore offer specialised data loading procedures and privacy budget accounting based on the Gaussian Differential Privacy framework, as well as automated modification of the user-supplied neural network architectures to ensure DP-conformity of its layers. We benchmark our framework’s computational performance against other open-source DP frameworks and evaluate its application on the paediatric pneumonia dataset, an image classification task and on the Medical Segmentation Decathlon Liver dataset in the task of medical image segmentation. We find that neural network training with rigorous privacy guarantees is possible while maintaining acceptable classification performance and excellent segmentation performance. Our framework compares favourably to related work with respect to memory consumption and computational performance. Our work presents an open-source software framework for differentially private deep learning, which we demonstrate in medical imaging analysis tasks. It serves to further the utilisation of privacy-enhancing ...
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correlation of in vivo imaging to morphomolecular pathology in translational research

Simone Ballke / Irina Heid / Carolin Mogler / Rickmer Braren / Markus Schwaiger / Wilko Weichert / Katja Steiger

EJNMMI Research, Vol 11, Iss 1, Pp 1-

challenge accepted

2021 Volume 9

Abstract: Abstract Correlation of in vivo imaging to histomorphological pathology in animal models requires comparative interdisciplinary expertise of different fields of medicine. From the morphological point of view, there is an urgent need to improve ... ...

Abstract	Abstract Correlation of in vivo imaging to histomorphological pathology in animal models requires comparative interdisciplinary expertise of different fields of medicine. From the morphological point of view, there is an urgent need to improve histopathological evaluation in animal model-based research to expedite translation into clinical applications. While different other fields of translational science were standardized over the last years, little was done to improve the pipeline of experimental pathology to ensure reproducibility based on pathological expertise in experimental animal models with respect to defined guidelines and classifications. Additionally, longitudinal analyses of preclinical models often use a variety of imaging methods and much more attention should be drawn to enable for proper co-registration of in vivo imaging methods with the ex vivo morphological read-outs. Here we present the development of the Comparative Experimental Pathology (CEP) unit embedded in the Institute of Pathology of the Technical University of Munich during the Collaborative Research Center 824 (CRC824) funding period together with selected approaches of histomorphological techniques for correlation of in vivo imaging to morphomolecular pathology.
Keywords	In vivo imaging ; Experimental pathology ; Animal models ; Translational research ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) Versus Autoimmune Pancreatitis (AIP)

Sebastian Ziegelmayer / Georgios Kaissis / Felix Harder / Friederike Jungmann / Tamara Müller / Marcus Makowski / Rickmer Braren

Journal of Clinical Medicine, Vol 9, Iss 4013, p

2020 Volume 4013

Abstract: The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based ... ...

Abstract	The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% ( n = 18) to discriminate between AIP or PDAC. Feature maps were plotted and visual difference was noted. The machine learning (ML) model achieved a sensitivity, specificity, and ROC-AUC of 0.89 ± 0.11, 0.83 ± 0.06, and 0.90 ± 0.02 for the deep features and 0.72 ± 0.11, 0.78 ± 0.06, and 0.80 ± 0.01 for the radiomic features. Visualization of feature maps indicated different activation patterns for AIP and PDAC. We successfully trained a machine learning model using deep feature extraction from CT-images to differentiate between AIP and PDAC. In comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC. Visualization of deep features could further improve the diagnostic accuracy of non-invasive differentiation of AIP and PDAC.
Keywords	deep learning ; radiomics ; pancreatic cancer ; autoimmune pancreatitis ; Medicine ; R
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multimodal graph attention network for COVID-19 outcome prediction

Matthias Keicher / Hendrik Burwinkel / David Bani-Harouni / Magdalini Paschali / Tobias Czempiel / Egon Burian / Marcus R. Makowski / Rickmer Braren / Nassir Navab / Thomas Wendler

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 14

Abstract: Abstract When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown. An accurate prediction of the ...

Abstract	Abstract When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients can often only be determined on short notice by acute indicators such as vital signs (e.g., breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic, multimodal graph-based approach combining imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph that shows a clustering of patients. For each patient in the graph, we extract radiomic features from a segmentation network that also serves as a latent image feature encoder. Together with clinical patient data like vital signs, demographics, and lab results, these modalities are combined into a multimodal representation of each patient. This feature extraction is trained end-to-end with an image-based Graph Attention Network to process the population graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation, and mortality. To combine multiple modalities, radiomic features are extracted from chest CTs using a segmentation neural network. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany and the publicly available iCTCF dataset show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention increases understanding of the patient relationships within the population graph and provides insight into the network’s decision-making process.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Efficient, high-performance semantic segmentation using multi-scale feature extraction.

Moritz Knolle / Georgios Kaissis / Friederike Jungmann / Sebastian Ziegelmayer / Daniel Sasse / Marcus Makowski / Daniel Rueckert / Rickmer Braren

PLoS ONE, Vol 16, Iss 8, p e

2021 Volume 0255397

Abstract: The success of deep learning in recent years has arguably been driven by the availability of large datasets for training powerful predictive algorithms. In medical applications however, the sensitive nature of the data limits the collection and exchange ... ...

Abstract	The success of deep learning in recent years has arguably been driven by the availability of large datasets for training powerful predictive algorithms. In medical applications however, the sensitive nature of the data limits the collection and exchange of large-scale datasets. Privacy-preserving and collaborative learning systems can enable the successful application of machine learning in medicine. However, collaborative protocols such as federated learning require the frequent transfer of parameter updates over a network. To enable the deployment of such protocols to a wide range of systems with varying computational performance, efficient deep learning architectures for resource-constrained environments are required. Here we present MoNet, a small, highly optimized neural-network-based segmentation algorithm leveraging efficient multi-scale image features. MoNet is a shallow, U-Net-like architecture based on repeated, dilated convolutions with decreasing dilation rates. We apply and test our architecture on the challenging clinical tasks of pancreatic segmentation in computed tomography (CT) images as well as brain tumor segmentation in magnetic resonance imaging (MRI) data. We assess our model's segmentation performance and demonstrate that it provides performance on par with compared architectures while providing superior out-of-sample generalization performance, outperforming larger architectures on an independent validation set, while utilizing significantly fewer parameters. We furthermore confirm the suitability of our architecture for federated learning applications by demonstrating a substantial reduction in serialized model storage requirement as a surrogate for network data transfer. Finally, we evaluate MoNet's inference latency on the central processing unit (CPU) to determine its utility in environments without access to graphics processing units. Our implementation is publicly available as free and open-source software.
Keywords	Medicine ; R ; Science ; Q
Subject code	006 ; 004
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Longitudinal Assessment of Health and Quality of Life of COVID-19 Patients Requiring Intensive Care—An Observational Study

Johanna Erber / Johannes R. Wießner / Gregor S. Zimmermann / Petra Barthel / Egon Burian / Fabian Lohöfer / Eimo Martens / Hrvoje Mijočević / Sebastian Rasch / Roland M. Schmid / Christoph D. Spinner / Rickmer Braren / Jochen Schneider / Tobias Lahmer

Journal of Clinical Medicine, Vol 10, Iss 5469, p

2021 Volume 5469

Abstract: Long-term health consequences in survivors of severe COVID-19 remain unclear. Eighteen COVID-19 patients admitted to the intensive care unit at the University Hospital Rechts der Isar, Munich, Germany, between 14 March and 23 June 2020, were ... ...

Abstract	Long-term health consequences in survivors of severe COVID-19 remain unclear. Eighteen COVID-19 patients admitted to the intensive care unit at the University Hospital Rechts der Isar, Munich, Germany, between 14 March and 23 June 2020, were prospectively followed-up at a median of 36, 75.5, 122 and 222 days after discharge. The health-related quality of life (HrQoL) (36-item Short Form Health Survey and St. George’s Respiratory Questionnaire, SGRQ), cardiopulmonary function, laboratory parameters and chest imaging were assessed longitudinally. The HrQoL assessment revealed a reduced physical functioning, as well as increased SGRQ impact and symptoms scores that all improved over time but remained markedly impaired compared to the reference groups. The median radiological severity scores significantly declined; persistent abnormalities were found in 33.3% of the patients on follow-up. A reduced diffusion capacity was the most common abnormal pulmonary function parameter. The length of hospitalization correlated with role limitations due to physical problems, the SGRQ symptom and the impact score. In conclusion, in survivors of severe COVID-19, the pulmonary function and symptoms improve over time, but impairments in their physical function and diffusion capacity can persist over months. Longer follow-up studies with larger cohorts will be necessary to comprehensively characterize long-term sequelae upon severe COVID-19 and to identify patients at risk.
Keywords	COVID-19 sequelae ; SARS-CoV-2 ; pulmonary function test ; health-related quality of life ; long-term health consequences ; Medicine ; R
Subject code	360
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Hyperpolarized 13 C Spectroscopy with Simple Slice-and-Frequency-Selective Excitation

Geoffrey J. Topping / Irina Heid / Marija Trajkovic-Arsic / Lukas Kritzner / Martin Grashei / Christian Hundshammer / Maximilian Aigner / Jason G. Skinner / Rickmer Braren / Franz Schilling

Biomedicines, Vol 9, Iss 2, p

2021 Volume 121

Abstract: Hyperpolarized 13 C nuclear magnetic resonance spectroscopy can characterize in vivo tissue metabolism, including preclinical models of cancer and inflammatory disease. Broad bandwidth radiofrequency excitation is often paired with free induction decay ... ...

Abstract	Hyperpolarized 13 C nuclear magnetic resonance spectroscopy can characterize in vivo tissue metabolism, including preclinical models of cancer and inflammatory disease. Broad bandwidth radiofrequency excitation is often paired with free induction decay readout for spectral separation, but quantification of low-signal downstream metabolites using this method can be impeded by spectral peak overlap or when frequency separation of the detected peaks exceeds the excitation bandwidth. In this work, alternating frequency narrow bandwidth (250 Hz) slice-selective excitation was used for 13 C spectroscopy at 7 T in a subcutaneous xenograft rat model of human pancreatic cancer (PSN1) to improve quantification while measuring the dynamics of injected hyperpolarized [1- 13 C]lactate and its metabolite [1- 13 C]pyruvate. This method does not require sophisticated pulse sequences or specialized radiofrequency and gradient pulses, but rather uses nominally spatially offset slices to produce alternating frequency excitation with simpler slice-selective radiofrequency pulses. Additionally, point-resolved spectroscopy was used to calibrate the 13 C frequency from the thermal proton signal in the target region. This excitation scheme isolates the small [1- 13 C]pyruvate peak from the similar-magnitude tail of the much larger injected [1- 13 C]lactate peak, facilitates quantification of the [1- 13 C]pyruvate signal, simplifies data processing, and could be employed for other substrates and preclinical models.
Keywords	magnetic resonance slice spectroscopy ; narrow bandwidth excitation ; point resolved spectroscopy ; hyperpolarized 13 C lactate ; rat subcutaneous tumor ; Biology (General) ; QH301-705.5
Subject code	333
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparison of definite chemoradiation therapy with carboplatin/paclitaxel or cisplatin/5-fluoruracil in patients with squamous cell carcinoma of the esophagus

Stefan Münch / Steffi U. Pigorsch / Michal Devečka / Hendrik Dapper / Wilko Weichert / Helmut Friess / Rickmer Braren / Stephanie E. Combs / Daniel Habermehl

Radiation Oncology, Vol 13, Iss 1, Pp 1-

2018 Volume 9

Abstract: Abstract Background While neoadjuvant chemoradiation therapy (nCRT) with subsequent surgery is the treatment of choice for patients with locally advanced or node-positive squamous cell carcinoma of the esophagus (SCC) suitable for surgery, patients who ... ...

Abstract	Abstract Background While neoadjuvant chemoradiation therapy (nCRT) with subsequent surgery is the treatment of choice for patients with locally advanced or node-positive squamous cell carcinoma of the esophagus (SCC) suitable for surgery, patients who are unsuitable for surgery or who refuse surgery should be treated with definite chemoradiation therapy (dCRT). Purpose of this study was to compare toxicity and oncologic outcome of dCRT with either cisplatin and 5-fluoruracil (CDDP/5FU) or carboplatin and paclitaxel (Carb/TAX) in patients with SCC. Methods Twenty-two patients who received dCRT with carboplatin (AUC2, weekly) and paclitaxel (50 mg per square meter of body-surface area, weekly) were retrospectively compared to 25 patients who were scheduled for dCRT with cisplatin (20 mg/m2/d) and 5-fluoruracil (500 mg/m2/d) on day 1–5 and day 29–33. For the per-protocol (PP) analysis, PP treatment was defined as complete radiation therapy with at least 54Gy and at least three complete cycles of Carb/TAX or complete radiation therapy with at least 54Gy and at least one complete cycle of CDDP/5FU. While patients who were scheduled for dCRT with Carb/TAX received a significantly higher total radiation dose (median dose 59.4Gy vs. 54Gy, p < 0.001) than patients who were scheduled for dCRT with CDDP/5FU, no significant differences were seen for other parameters (age, sex, TNM-stage, grading and tumor extension). Results Forty-seven patients (25 patients treated with CDDP/5FU and 22 patients treated with Carb/TAX) were evaluated for the intention-to-treat (ITT) analysis and 41 of 47 patients (23 patients treated with CDDP/5FU and 18 patients treated with Carb/TAX) were evaluated for the PP analysis. Severe myelotoxicity (≥ III°) was seen in 52% (CDDP/5FU) and 55% of patients (Carb/TAX), respectively (p = 1.000). In the univariate binary logistic regression analysis, patients age was the only factor associated with an increased risk of ≥ III° myelotoxicity (hazard ratio 1.145, 95% CI 1.035; 1.266; p = 0.009). ...
Keywords	Squamous cell carcinoma of the esophagus ; Definite chemoradiation ; Cisplatin/5-fluoruracil ; Carboplatin/paclitaxel ; Medical physics. Medical radiology. Nuclear medicine ; R895-920 ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Subject code	616 ; 610
Language	English
Publishing date	2018-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Author Correction

Elisabeth Bliemsrieder / Georgios Kaissis / Martin Grashei / Geoffrey Topping / Jennifer Altomonte / Christian Hundshammer / Fabian Lohöfer / Irina Heid / Dominik Keim / Selamawit Gebrekidan / Marija Trajkovic-Arsic / AM Winkelkotte / Katja Steiger / Roman Nawroth / Jens Siveke / Markus Schwaiger / Marcus Makowski / Franz Schilling / Rickmer Braren

Scientific Reports, Vol 11, Iss 1, Pp 1-

Hyperpolarized 13C pyruvate magnetic resonance spectroscopy for in vivo metabolic phenotyping of rat HCC

2021 Volume 1

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper. ...

Abstract	An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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