Tobias Lasser

FAR-LIO: Enabling High-Speed Autonomy through Fast, Accurate, and Robust LiDAR-Inertial Odometry

Maximilian Leitenstern, Marcel Weinmann, Patrick Haft, Tobias Lasser et autres

Robust and accurate odometry estimation is essential in modern robotics. In environments characterized by highly dynamic motion and sensor noise, odometry estimation becomes increasingly challenging. Autonomous racing combines both factors in an unstructured setting, where minimizing odometry latency is essential for stable …

FAR-LIO: Enabling High-Speed Autonomy through Fast, Accurate, and Robust LiDAR-Inertial Odometry

Maximilian Leitenstern, Marcel Weinmann, Patrick Haft, Tobias Lasser et autres

Robust and accurate odometry estimation is essential in modern robotics. In environments characterized by highly dynamic motion and sensor noise, odometry estimation becomes increasingly challenging. Autonomous racing combines both factors in an unstructured setting, where minimizing odometry latency is essential for stable …

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ShortcutBreaker: Low-Rank Noisy Bottleneck and Frequency Filtering Block for Multi-Class Unsupervised Anomaly Detection

Peng Tang, Xiaobin Hu, Tingcheng Li, Yang Nan et autres

Multi-class unsupervised anomaly detection (MUAD) has garnered growing research interest, as it seeks to develop a unified model for anomaly detection across multiple classes, i.e., eliminating the need to train separate models for distinct objects and thereby saving substantial computational resources. Under …

Anomaly Detection in Medical Images Using Encoder-Attention-2Decoders Reconstruction

Peng Tang, Xiaoxiao Yan, Xiaobin Hu, Kai Wu et autres

Anomaly detection (AD) in medical applications is a promising field, offering a cost-effective alternative to labor-intensive abnormal data collection and labeling. However, the success of feature reconstruction-based methods in AD is often hindered by two critical factors: the domain gap of pre-trained …

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A global phase retrieval method for continuously acquired grating-based dark-field computed tomography

Jakob Haeusele, Clemens Schmid, Josepha Hilmer, Florian Schaff et autres

Grating-based dark-field and phase x-ray imaging aims to extend conventional attenuation-based x-ray imaging by revealing additional microstructural information. A big challenge for translating grating-based dark-field computed tomography to medical applications lies in minimizing the data acquisition time. While a continuously moving detector …

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A novel phase retrieval method for continuously acquired grating-based dark-field computed tomography using Tikhonov regularization

Jakob Haeusele, Clemens Schmid, Josepha Hilmer, Florian Schaff et autres

Grating-based dark-field and phase x-ray imaging aims to extend conventional attenuation-based x-ray imaging by revealing additional microstructural information. A big challenge for translating grating-based dark-field computed tomography to medical applications lies in minimizing the data acquisition time. While a continuously moving detector …

de (code pays fourni par la source)

Einblicke und Herausforderungen bei der Implementierung von Vision Transformern in der Thoraxradiographie

Sardi Hyska, Alessandro Wollek, Michael Ingrisch, Tobias Lasser et autres

Zielsetzung Ziel dieser Studie war die Bewertung eines Vision Transformer (ViT)-basierten KI-Modells, das auf öffentlich zugänglichen Röntgen-Thorax-Aufnahmen (Rö-Tx) trainiert wurde, bei der Anwendung auf reale Klinikdaten. Untersucht wurden die Performance in der Erkennung von Pleuraerguss, Pneumothorax, Kardiomegalie und Konsolidierungen sowie potenzielle Störfaktoren. …

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Statistical Reconstruction For Anisotropic X-ray Dark-Field Tomography

David L. Frank, Cederik Höfs, Tobias Lasser

Anisotropic X-ray Dark-Field Tomography (AXDT) is a novel imaging technology that enables the extraction of fiber structures on the micrometer scale, far smaller than standard X-ray Computed Tomography (CT) setups. Directional and structural information is relevant in medical diagnostics and material testing. …

Artificial Intelligence and Statistical Methods in Clinical and Industrial Vision Applications

Theodor Cheslerean-Boghiu, Tobias Lasser, Technische Universität München

Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging

Tina Dorosti, Manuel Schultheiß, Felix Hofmann, Johannes Thalhammer et autres

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7194 contrast-enhanced CT …

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Enhanced Phase Retrieval for Continuous Grating-Based Dark-Field CT via Global Tikhonov Regularization

Jakob Haeusele, Clemens Schmid, Josepha Hilmer, Florian Schaff et autres

Grating-based dark-field and phase contrast x-ray imaging seeks to enhance conventional attenuation based x-ray imaging by improving soft-tissue contrast and revealing additional microstructural details. Initial studies have shown that the dark-field channel in particular significantly complements and enhances conventional thoracic radiography. The …

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Improving the Precision of CNNs for Magnetic Resonance Spectral Modeling

John LaMaster, Dhritiman Das, Florian Kofler, Jason C. Crane et autres

Magnetic resonance spectroscopic imaging is a widely available imaging modality that can non-invasively provide a metabolic profile of the tissue of interest, yet is challenging to integrate clinically. One major reason is the expensive, expert data processing and analysis that is required. …