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Multilingual Natural Language Processing Model for Radiology Reports The Summary is all you need!

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arXiv 2023年

作者： Lindo, Mariana Santos, Ana Sofia Ferreira, André Li, Jianning Luijten, Gijs Correia, Gustavo Kim, Moon Schaarschmidt, Benedikt Michael Deuschl, Cornelius Haubold, Johannes Kleesiek, Jens Egger, Jan Alves, Victor ALGORITMI Research Centre/LASI University of Minho Braga Portugal Essen Germany Essen Germany Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

The impression section of a radiology report summarizes important radiology findings and plays a critical role in communicating these findings to physicians. However, the preparation of these summaries is time-consuming and error-prone for radiologists. Recently, numerous models for radiology report summarization have been developed. Nevertheless, there is currently no model that can summarize these reports in multiple languages. Such a model could greatly improve future research and the development of Deep Learning models that incorporate data from patients with different ethnic backgrounds. In this study, the generation of radiology impressions in different languages was automated by fine-tuning a model, publicly available, based on a multilingual text-to-text Transformer to summarize findings available in English, Portuguese, and German radiology reports. In a blind test, two board-certified radiologists indicated that for at least 70% of the system-generated summaries, the quality matched or exceeded the corresponding human-written summaries, suggesting substantial clinical reliability. Furthermore, this study showed that the multilingual model outperformed other models that specialized in summarizing radiology reports in only one language, as well as models that were not specifically designed for summarizing radiology reports, such as ChatGPT. Copyright © 2023, The Authors. All rights reserved.

关键词： Radiology

FAST3D: Flow-Aware Self-Training for 3D Object Detectors 32

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FAST3D: Flow-Aware Self-Training for 3D Object Detectors

32nd British Machine vision Conference, BMVC 2021

作者： Fruhwirth-Reisinger, Christian Opitz, Michael Possegger, Horst Bischof, Horst Christian Doppler Laboratory for Embedded Machine Learning Austria Institute of Computer Graphics and Vision Graz University of Technology Austria Amazon

In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors. This eliminates the need for expensive, high-quality labels whenever the environment changes (e.g. geographic location, sensor setup, weather condition). State-of-the-art self-training approaches, however, mostly ignore the temporal nature of autonomous driving data. To address this issue, we propose a flow-aware self-training method that enables unsupervised domain adaptation for 3D object detectors on continuous LiDAR point clouds. In order to get reliable pseudo-labels, we leverage scene flow to propagate detections through time. In particular, we introduce a flow-based multi-target tracker that exploits flow consistency to filter and refine resulting tracks. The emerged precise pseudo-labels then serve as a basis for model re-training. Starting with a pre-trained KITTI model, we conduct experiments on the challenging Waymo Open Dataset to demonstrate the effectiveness of our approach. Without any prior target domain knowledge, our results show a significant improvement over the state-of-the-art. © 2021. The copyright of this document resides with its authors.

关键词： Autonomous vehicles

pyCEPS: A cross-platform Electroanatomic Mapping Data to Computational Model Conversion Platform for the Calibration of Digital Twin Models of Cardiac Electrophysiology

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arXiv 2024年

作者： Arnold, Robert Prassl, Anton J. Neic, Aurel Thaler, Franz Augustin, Christoph M. Gsell, Matthias A.F. Gillette, Karli Manninger, Martin Scherr, Daniel Plank, Gernot Gottfried Schatz Research Center Division of Medical Physics and Biophysics Medical University of Graz Graz Austria NumeriCor GmbH Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria BioTechMed-Graz Graz Austria Division of Cardiology Department of Medicine Medical University of Graz Graz Austria

Background and Objective: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community. Methods and Results: pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems;(ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs);(iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model. Conclusion: With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data. © 2024, CC BY-NC-ND.

关键词： Mapping

CUDA and Applications to Task-based Programming

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CUDA and Applications to Task-based Programming

42nd Annual Conference on European Association for computer graphics, EUROgraphics 2021

作者： Kenzel, M. Kerbl, B. Winter, M. Steinberger, M. Saarland University Computer Graphics Lab Germany TU Wien Institute of Visual Computing and Human-Centered Technology Austria Graz University of Technology Institute of Computer Graphics and Vision Austria

ISBN: (纸本)9783038681359

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. © 2021 The Author(s).

关键词： Scheduling

CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions

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arXiv 2023年

作者： Thaler, Franz Gsell, Matthias A.F. 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 Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely established to assess the viability of myocardial tissue of patients after acute myocardial infarction (MI). We propose the Cascading Refinement CNN (CaRe-CNN), which is a fully 3D, end-to-end trained, 3-stage CNN cascade that exploits the hierarchical structure of such labeled cardiac data. Throughout the three stages of the cascade, the label definition changes and CaRe-CNN learns to gradually refine its intermediate predictions accordingly. Furthermore, to obtain more consistent qualitative predictions, we propose a series of post-processing steps that take anatomical constraints into account. Our CaRe-CNN was submitted to the FIMH 2023 MYOSAIQ challenge, where it ranked second out of 18 participating teams. CaRe-CNN showed great improvements most notably when segmenting the difficult but clinically most relevant myocardial infarct tissue (MIT) as well as microvascular obstructions (MVO). When computing the average scores over all labels, our method obtained the best score in eight out of ten metrics. Thus, accurate cardiac segmentation after acute MI via our CaRe-CNN allows generating patient-specific models of the heart serving as an important step towards personalized medicine. © 2023, CC BY-SA.

关键词： Image segmentation

HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans

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arXiv 2023年

作者： Ainetter, Stefan Stekovic, Sinisa Fraundorfer, Friedrich Lepetit, Vincent Institute for Computer Graphics and Vision Graz University of Technology Graz Austria LIGM École des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France

We present an automated and efficient approach for retrieving high-quality CAD models of objects and their poses in a scene captured by a moving RGB-D camera. We first investigate various objective functions to measure similarity between a candidate CAD object model and the available data, and the best objective function appears to be a"render-and-compare" method comparing depth and mask rendering. We thus introduce a fast-search method that approximates an exhaustive search based on this objective function for simultaneously retrieving the object category, a CAD model, and the pose of an object given an approximate 3D bounding box. This method involves a search tree that organizes the CAD models and object properties including object category and pose for fast retrieval and an algorithm inspired by Monte Carlo Tree Search, that efficiently searches this tree. We show that this method retrieves CAD models that fit the real objects very well, with a speed-up factor of 10x to 120x compared to exhaustive search. Copyright © 2023, The Authors. All rights reserved.

关键词： computer aided design

TAEC: Unsupervised Action Segmentation with Temporal-Aware Embedding and Clustering

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arXiv 2023年

作者： Lin, Wei Kukleva, Anna Possegger, Horst Kuehne, Hilde Bischof, Horst Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Austria Max-Planck-Institute for Informatics Germany Goethe University Frankfurt Germany

Temporal action segmentation in untrimmed videos has gained increased attention recently. However, annotating action classes and frame-wise boundaries is extremely time consuming and cost intensive, especially on large-scale datasets. To address this issue, we propose an unsupervised approach for learning action classes from untrimmed video sequences. In particular, we propose a temporal embedding network that combines relative time prediction, feature reconstruction, and sequence-to-sequence learning, to preserve the spatial layout and sequential nature of the video features. A two-step clustering pipeline on these embedded feature representations then allows us to enforce temporal consistency within, as well as across videos. Based on the identified clusters, we decode the video into coherent temporal segments that correspond to semantically meaningful action classes. Our evaluation on three challenging datasets shows the impact of each component and, furthermore, demonstrates our state-of-the-art unsupervised action segmentation results. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

ROStopbox: A Human-Centered Vehicle Environment Data Acquisition System

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ROStopbox: A Human-Centered Vehicle Environment Data Acquisi...

Automated Vehicle Validation Conference (IAVVC), IEEE International

作者： Daniel Schneider Jonas Nesensohn Bernhard Schick Daniel Watzenig Institute for Advanced Driver Assistance Systems and Connected Mobility Kempten University of Applied Sciences Institute of Computer Graphics and Vision Graz University of Technology AVL Deutschland GmbH Advanced Solution Lab Karlsruhe

ISBN: (数字)9798350354072

ISBN: (纸本)9798350354089

As the development towards fully automated driving continues, passenger trust in automation and human well-being when using advanced driver assistance systems will become increasingly important. To support the existing data-driven development process, systems for acquiring perceptual data such as from camera and LiDAR along with physiological and subjective information will be required in the future. The simultaneous recording of physiological signals alongside data from traditional perception sensors can therefore help to identify scenarios in which passengers are stressed, have an increased workload, or feel unsafe. By recording questionnaires in parallel, it is possible to also correlate the physiological signals with passengers’ subjective perception. The combination of these signal sources makes it possible to identify causes of drivers’ increased workload in the environmental data, describe development targets, and safeguard rules for future assistance systems and automated driving functions, keeping humans in the loop. Here, we present an approach for the simultaneous acquisition of sensor information from multiple domains such as physiological measurement data, environmental sensors, vehicle-internal communication, and subjective questionnaires, paying attention to an unobtrusive measurement setup. We demonstrate the benefits of our approach by identifying a relevant scenario in real traffic conditions in which we observe the impact on the driver’s physiological parameters.

关键词： Laser radar Operating systems Data acquisition Human factors Cameras Physiology Human in the loop Sensors Recording Testing

A Visual Surveillance System to Observe Realistic Road User Behavior for Improved Pedestrian and Cyclist Safety at Crossroads

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A Visual Surveillance System to Observe Realistic Road User ...

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Nadezda Kirillova Horst Possegger Horst Bischof Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

Pedestrians and cyclists suffer the most serious injuries in traffic accidents. Existing Pedestrian Protection Systems and Road Safety Systems rely on an ideal model of pedestrian behavior and do not consider that people tend to take shortcuts, appear at unexpected places or can be distracted on the road, for example, by using a smartphone or wearing headphones. Collecting and analyzing realistic road user behavior is a crucial component to improve pedestrian and cyclist safety. However, such real-world data is still missing. To address this, we propose a visual surveillance system with two perpendicular partially overlapping fields of view, combined with a fully automated deep learning-based pipeline to process and collect video observations, detect and extract road user trajectories in real-world coordinates and estimate human attributes, such as age, gender, smartphone usage, etc. We demonstrate our prototype by deploying it in two locations in a European city.

关键词： Visualization Smart cities Surveillance Prototypes Streaming media Road safety Behavioral sciences