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19 Publications visible to you, out of a total of 19
Implementation Techniques for SPMD Kernels on CPUs

Abstract

Not specified

Authors: Joachim Meyer, Aksel Alpay, Sebastian Hack, Holger Fröning, Vincent Heuveline

Date Published: 18th Apr 2023

Publication Type: Journal

One Pass to Bind Them: The First Single-Pass SYCL Compiler with Unified Code Representation Across Backends

Abstract

Not specified

Authors: Aksel Alpay, Vincent Heuveline

Date Published: 18th Apr 2023

Publication Type: Journal

ACSEG: AUTOMATED 3D CYTOPLASM SEGMENTATION IN SOFT X-RAY TOMOGRAPHY

Abstract (Expand)

The structure of cells is a key to understanding cellular function, diagnosis of pathological conditions, and development of new treatments. Soft X-ray tomography (SXT) is a unique tool to image cellular … structure without fixation or labeling at high spatial resolution and throughput. Ongoing improvements in faster acquisition times increase demand for accelerated image analysis. Currently, the automatic segmentation of cellular structures is a major bottleneck in the SXT data analysis pipeline. In this paper, we introduce an automated 3D cytoplasm segmentation model - ACSeg - by use of semi-automatically segmented labels and 3D U-Net, implemented in the online platform Biomedisa. The segmentation model is trained on semi-automatically labeled datasets and shows rapid convergence to high-accuracy segmentation, therefore reducing time and labor. ACSeg trained on 43 SXT tomograms of human immune T cells, the model successfully segmented unseen SXT tomograms of human hepatocyte-derived carcinoma cells, mouse microglia, and embryonic fibroblast cells. Furthermore, we could diversify the model by adding only 6 specific SXT tomograms, showing the potential for the development of an optimal experimental design. The ACSeg is published on the open image segmentation platform Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types. The approach can be expanded for automatic segmentation of other organelles visualized by SXT, providing means for structural analysis of cell remodeling under different pathogens at statistically significant sizes, therefore enabling the development of novel drug treatments.

Authors: Ayse Erozan, Philipp Lösel, Vincent Heuveline, Venera Weinhardt

Date Published: 5th Apr 2023

Publication Type: Journal

VTABLE HIJACKING: OBJECT TYPE INTEGRITY FOR RUN- TIME TYPE INFORMATION

Abstract (Expand)

Attackers try to hijack the control-flow of a victim’s process by exploiting a run-time vulnerability. Vtable hijacking is a state-of-the-art technique adversaries use to conduct control-flow hijacking … attacks. It abuses the reliance of language constructs related to polymorphism on dynamic type information. The Control Flow Integrity (CFI) security policy is a well-established solution designed to prevent attacks that corrupt the control-flow. Deployed defense mechanisms based on CFI are often generic, which means that they do not consider high-level programming language semantics. This makes them vulnerable to vtable hijacking attacks. Object Type Integrity (OTI) is an orthogonal security policy that specifically addresses vtable hijacking. CFIXX is a Clang compiler extension that enforces OTI in the context of dynamic dispatch, which prevents vtable hijacking in this setting. However, this extension does not enforce OTI in context of polymorphism. The contribution of this work is a practical implementation to enable OTI in the context of C++’s run-time type information for the dynamic_cast expressions and the typeid operator.

Authors: Marco Schröder, Stefan Machmeier, Vincent Heuveline

Date Published: 2nd Mar 2023

Publication Type: Journal

aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment

Abstract

Not specified

Authors: Julian Schröter, Tal Dattner, Jennifer Hüllein, Alejandra Jayme, Vincent Heuveline, Georg F. Hoffmann, Stefan Kölker, Dominic Lenz, Thomas Opladen, Bernt Popp, Christian P. Schaaf, Christian Staufner, Steffen Syrbe, Sebastian Uhrig, Daniel Hübschmann, Heiko Brennenstuhl

Date Published: 2023

Publication Type: Journal

Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning

Abstract (Expand)

Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method …. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.

Authors: Yaroslav Zharov, Evelina Ametova, Rebecca Spiecker, Tilo Baumbach, Genoveva Burca, Vincent Heuveline

Date Published: 2023

Publication Type: Journal

Review of Deep Learning Methods for Individual Treatment Effect Estimation with Automatic Hyperparameter Optimization

Abstract (Expand)

<p>Abstract—Estimation of individual treatment effect (ITE) for different types of treatment is a common challenge in therapy assessments, clinical trials and diagnosis. Deep learning methods, …ing methods, namely representation based, adversarial, and variational, have shown promising potential in ITE estimation. However, it was unclear whether the hyperparameters of the originally proposed methods were well optimized for different benchmark datasets. To solve these problems, we created a public code library containing representation-based, adversarial, and variational methods written in TensorFlow. In order to have a broader collection of ITE estimation methods, we have also included neural network based meta-learners. The code library is made accessible for reproducibility and facilitating future works in the field of causal inference. Our results demonstrate that performance of most methods can be improved using automatic hyperparameter optimization. Additionally, we review the methods and compare the performance of the optimized models from our library on publicly available datasets. The potential of hyperparameter optimization may encourage researchers to focus on this aspect when creating new methods for inferring individual treatment effect.</p>

Authors: Andrei Sirazitdinov, Marcus Buchwald, Jürgen Hesser, Vincent Heuveline

Date Published: 6th Dec 2022

Publication Type: Misc

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