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IEEE Workshop on Applications of computer vision (WACV)

作者： Robert Harb Thomas Pock Heimo Müller Institute of Computer Graphics and Vision Graz University of Technology Austria Diagnostic and Research Institute of Pathology Medical University of Graz Austria

We present a novel diffusion-based approach to generate synthetic histopathological Whole Slide Images (WSIs) at an unprecedented gigapixel scale. Synthetic WSIs have many potential applications: They can augment training datasets to enhance the performance of many computational pathology applications. They allow the creation of synthesized copies of datasets that can be shared without violating privacy regulations. Or they can facilitate learning representations of WSIs without requiring data annotations. Despite this variety of applications, no existing deep-learning-based method generates WSIs at their typically high resolutions. Mainly due to the high computational complexity. Therefore, we propose a novel coarse-to-fine sampling scheme to tackle image generation of high-resolution WSIs. In this scheme, we increase the resolution of an initial low-resolution image to a high-resolution WSI. Particularly, a diffusion model sequentially adds fine details to images and increases their resolution. In our experiments, we train our method with WSIs from the TCGA-BRCA dataset. Additionally to quantitative evaluations, we also performed a user study with pathologists. The study results suggest that our generated WSIs resemble the structure of real WSIs.

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LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation 1st

LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar ...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Thaler, Franz Štern, Darko Plank, Gernot Urschler, Martin Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria AVL List GmbH Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

ISBN: (纸本)9783031870088

Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. Thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ablation

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Attribute Relevance and Discretisation in Knowledge Discovery: A Study in Stylometric Domain 23rd

Attribute Relevance and Discretisation in Knowledge Discov...

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23rd International Conference on Computational Science, ICCS 2023

作者： Stańczyk, Urszula Zielosko, Beata Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 Sosnowiec41-200 Poland

ISBN: (纸本)9783031360206

The paper demonstrates the research methodology focused on observations of relations between attribute relevance, displayed by rankings, and discretisation. Instead of transforming all continuous attributes before data exploration, the variables were gradually processed, and the impact of such a change on the performance of a classifier was studied. Considerable experiments carried out on stylometric data illustrate that selective discretisation could be more advantageous to predictive accuracy than some uniform transformation of all features. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metadata

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Augmentation-Based Domain Generalization and Joint Training from Multiple Source Domains for Whole Heart Segmentation 1st

Augmentation-Based Domain Generalization and Joint Training...

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ISBN: (纸本)9783031870088

As the leading cause of death worldwide, cardiovascular diseases motivate the development of more sophisticated methods to analyze the heart and its substructures from medical images like Computed Tomography (CT) and Magnetic Resonance (MR). Semantic segmentations of important cardiac structures that represent the whole heart are useful to assess patient-specific cardiac morphology and pathology. Furthermore, accurate semantic segmentations can be used to generate cardiac digital twin models which allows e.g. electrophysiological simulation and personalized therapy planning. Even though deep learning-based methods for medical image segmentation achieved great advancements over the last decade, retaining good performance under domain shift - i.e. when training and test data are sampled from different data distributions - remains challenging. In order to perform well on domains known at training-time, we employ a (1) balanced joint training approach that utilizes CT and MR data in equal amounts from different source domains. Further, aiming to alleviate domain shift towards domains only encountered at test-time, we rely on (2) strong intensity and spatial augmentation techniques to greatly diversify the available training data. Our proposed whole heart segmentation method, a 5-fold ensemble with our contributions, achieves the best performance for MR data overall and a performance similar to the best performance for CT data when compared to a model trained solely on CT. With 93.33% DSC and 0.8388 mm ASSD for CT and 89.30% DSC and 1.2411 mm ASSD for MR data, our method demonstrates great potential to efficiently obtain accurate semantic segmentations from which patient-specific cardiac twin models can be generated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Towards Large-Scale Video-Based Highway Traffic Monitoring

Towards Large-Scale Video-Based Highway Traffic Monitoring

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International Conference on Intelligent Transportation

作者： Johannes Spoecklberger Jakub Micorek Horst Possegger Horst Bischof Faculty of Computer Science Institute of Computer Graphics and Vision (ICG) Graz University of Technology Graz Austria

Traffic safety on highways is supported by a variety of technical measures, including countless camera systems that are often only monitored by human operators. However, due to the sheer amount of data, safety monitoring and accident prevention are limited by human resources. In this paper, we present an efficient system capable of extracting accurate vehicle trajectories from the vast amount of video data generated by modern highway infrastructures. Our proposed system conveniently leverages bird's eye view transformations estimated from aerial data or street marker geometry to generate geo-localized trajectories. Utilizing existing infrastructure, we demonstrate that the central data for video-based highway traffic monitoring can be reliably extracted. Remarkably, this can be achieved solely relying on uncalibrated cameras and noisy video streams.

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Robots Saving Lives: A Literature Review About Search and Rescue (SAR) in Harsh Environments

Robots Saving Lives: A Literature Review About Search and Re...

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IEEE Symposium on Intelligent Vehicle

作者： Kailin Tong Yuxi Hu Berin Dikic Selim Solmaz Friedrich Fraundorfer Daniel Watzenig Virtual Vehicle Research GmbH Graz Austria Institute of Computer Graphics and Vision at Graz University of Technology Graz Austria

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In recent years, the rise in both natural and man-made disasters, along with armed conflicts and terrorist threats, has elevated the demand for Search and Rescue (SAR) missions worldwide. This paper underscores the critical necessity to enhance SAR capacity, safety, and capabilities, with a primary goal of reducing response times through the integration of robots into SAR operations. The examination of research on robotized SAR highlights deficiencies in both software and hardware, particularly focusing on perception systems for robotized SAR platforms.

关键词： Intelligent vehicles Terrorism Disasters Bibliographies Focusing Software Hardware

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MultiAR: A Multi-User Augmented Reality Platform for Biomedical Education 46

MultiAR: A Multi-User Augmented Reality Platform for Biomedi...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Perz, Markus Luijten, Gijs Kleesiek, Jens Schmalstieg, Dieter Egger, Jan Gsaxner, Christina West German Cancer Center Cancer Research Center Cologne Essen Essen Germany University of Stuttgart Institute for Visualization and Interactive Systems Stuttgart Germany Graz University of Technology Institute of Computer Graphics and Vision Graz Austria University Medicine Essen Institute for Artificial Intelligence in Medicine Essen Germany Essen Germany

ISBN: (纸本)9798350371499

This paper addresses the growing integration of Augmented Reality (AR) in biomedical sciences, emphasizing collaborative learning experiences. We present MultiAR, a versatile, domain-specific platform enabling multi-user interactions in AR for biomedical education. Unlike platform-specific solutions, MultiAR supports various AR devices, including handheld and head-mounted options. The framework extends across domains, augmenting biomedical education applications with collaborative capabilities. We define essential requirements for a multi-user AR framework in education, detail MultiAR's design and implementation, and comprehensively evaluate it using anatomy education examples. Quantitative and qualitative analyses, covering system performance, accuracy metrics, and a user study with 20 participants, highlight the urgent need for a tailored collaborative AR platform in biomedical education. Results underscore enthusiasm for collaborative AR technology, endorsing MultiAR as an accessible, versatile solution for developers and end-users in biomedical education. © 2024 IEEE.

关键词： Augmented reality

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GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm

arXiv

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arXiv 2023年

作者： Mohr, Ludwig Geles, Ismail Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria

Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitable initialization and in the presence of a high outlier rate or in the case of small overlap though the task of rigid registration still presents great challenges. The advent of deep learning in computer vision has brought new drive to research on this topic, since it provides the possibility to learn expressive feature-representations and provide one-shot estimates instead of depending on time-consuming iterations of conventional robust methods. Yet, the rotation and permutation invariant nature of point clouds poses its own challenges to deep learning, resulting in loss of performance and low generalization capability due to sensitivity to outliers and characteristics of 3D scans not present during network training. In this work, we present a novel fast and light-weight network architecture using the attention mechanism to augment point descriptors at inference time to optimally suit the registration task of the specific point clouds it is presented with. Employing a fully-connected graph both within and between point clouds lets the network reason about the importance and reliability of points for registration, making our approach robust to outliers, low overlap and unseen data. We test the performance of our registration algorithm on different registration and generalization tasks and provide information on runtime and resource consumption. The code and trained weights are available at https://***/mordecaimalignatius/GAFAR/. Copyright © 2023, The Authors. All rights reserved.

关键词： computer vision

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CUDA and Applications to Task-based Programming

CUDA and Applications to Task-based Programming

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43rd Annual Conference on European Association for computer graphics, EUROgraphics 2022

作者： Kerbl, Bernhard Kenzel, Michael Winter, Martin Steinberger, Markus TU Wien Institute of Visual Computing and Human-Centered Technology Austria Saarland University Computer Graphics Lab Germany Intelligent Cloud Rendering Laboratory Huawei Technologies Austria Graz University of Technology Institute of Computer Graphics and Vision Austria

ISBN: (纸本)9783038681724

Since its inception, the CUDA programming model has been continuously evolving. Because the CUDA toolkit aims to consistently expose cutting-edge capabilities for general-purpose compute jobs to its users, the added features in each new version reflect the rapid changes that we observe in GPU architectures. Over the years, the changes in hardware, growing scope of built-in functions and libraries, as well as an advancing C++ standard compliance have expanded the design choices when coding for CUDA, and significantly altered the directives to achieve peak performance. In this tutorial, we give a thorough introduction to the CUDA toolkit, demonstrate how a contemporary application can benefit from recently introduced features and how they can be applied to task-based GPU scheduling in particular. For instance, we will provide detailed examples of use cases for independent thread scheduling, cooperative groups, and the CUDA standard library, libcu++, which are certain to become an integral part of clean coding for CUDA in the near future. © 2022 The Author(s)

关键词： Regulatory compliance

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Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview

arXiv

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arXiv 2024年

作者： Auras, Alexander Gandikota, Kanchana Vaishnavi Droege, Hannah Moeller, Michael Institute for Vision and Graphics University of Siegen NRW Germany Institute of Computer Science Rheinische Friedrich-Wilhelms-Universität Bonn NRW Germany

This paper provides an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one-dimensional toy problem are provided, showing the robustness of different approaches and empirically verifying theoretical guarantees. Another focus of this review is the exploration of the subspace of data-consistent solutions through explicit guidance to satisfy specific semantic or textural properties. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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