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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Pooyan, Melika Awwad, Hadeel García, Eloy Martí, Robert Institute of Computer Vision and Robotics University of Girona Girona Spain

ISBN: (纸本)9783031777882

Integrating 2D mammography with 3D magnetic resonance imaging (MRI) is crucial for improving breast cancer diagnosis and treatment planning. However, this integration is challenging due to differences in imaging modalities and the need for precise tissue segmentation and alignment. This paper addresses these challenges by enhancing biomechanical breast models in two main aspects: improving tissue identification using nnU-Net segmentation models and evaluating finite element (FE) biomechanical solvers, specifically comparing NiftySim and FEBio. We performed a detailed six-class segmentation of breast MRI data using the nnU-Net architecture, achieving Dice Coefficients of 0.94 for fat, 0.88 for glandular tissue, and 0.87 for pectoral muscle. The overall foreground segmentation reached a mean Dice Coefficient of 0.83 through an ensemble of 2D and 3D U-Net configurations, providing a solid foundation for 3D reconstruction and biomechanical modeling. The segmented data was then used to generate detailed 3D meshes and develop biomechanical models using NiftySim and FEBio, which simulate breast tissue’s physical behaviors under compression. Our results include a comparison between NiftySim and FEBio, providing insights into the accuracy and reliability of these simulations in studying breast tissue responses under compression. The findings of this study have the potential to improve the integration of 2D and 3D imaging modalities, thereby enhancing diagnostic accuracy and treatment planning for breast cancer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tissue

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Graph Neural Networks for Modelling Breast Biomechanical Compression 1st

Graph Neural Networks for Modelling Breast Biomechanical Co...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Awwad, Hadeel García, Eloy Martí, Robert Computer Vision and Robotics Institute University of Girona Girona Spain

ISBN: (纸本)9783031777882

Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating soft tissue deformation, it struggles with balancing accuracy and computational efficiency. Recent studies have used data-driven models trained on FEA results to speed up tissue deformation predictions. We propose to explore Physics-based Graph Neural Networks (PhysGNN) for breast compression simulation. PhysGNN has been used for data-driven modelling in other domains, and this work presents the first investigation of their potential in predicting breast deformation during mammographic compression. Unlike conventional data-driven models, PhysGNN, which incorporates mesh structural information and enables inductive learning on unstructured grids, is well-suited for capturing complex breast tissue geometries. Trained on deformations from incremental FEA simulations, PhysGNN’s performance is evaluated by comparing predicted nodal displacements with those from finite element (FE) simulations. This deep learning (DL) framework shows promise for accurate, rapid breast deformation approximations, offering enhanced computational efficiency for real-world scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tissue

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FoundationGrasp: Generalizable Task-Oriented Grasping With Foundation Models

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 12418-12435页

作者： Tang, Chao Huang, Dehao Dong, Wenlong Xu, Ruinian Zhang, Hong Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain regions responsible for semantic and geometric reasoning during cognitive processes, modeling the intricate relationship between objects, tasks, and grasps neces-sitates rich semantic and geometric prior knowledge about these elements. Existing methods typically restrict the prior knowledge to a closed-set scope, limiting their generalization to novel objects and tasks out of the training set. To addresssuch a limitation, we propose FoundationGrasp, a foundationmodel-based TOG framework that leverages the open-ended knowledge from foundation models to learn generalizable TOG skills. Extensive experiments are conducted on the contributed Language and vision Augmented TaskGrasp (LaViA-TaskGrasp)dataset, demonstrating the superiority of FoundationGrasp over existing methods when generalizing to novel object instances,object classes, and tasks out of the training set. Furthermore,the effectiveness of FoundationGrasp is validated in real-robot grasping and manipulation experiments on a 7-DoF robotic arm. © 2004-2012 IEEE.

关键词： Robotic arms

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A Hierarchical Progressive Perception System for Autonomous Luggage Trolley Collection

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14484-14497页

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China Southern University of Science and Technology Jiaxing Research Institute Jiaxing314000 China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China

Advancements in intelligent vehicle technologies and autonomous driving are now used for tasks like collecting luggage trolleys at airports. The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys. However, existing methods for detecting and locating luggage trolleys often fail when they are not fully visible. To address this, we introduce the hierarchical progressive perception system, which enhances the detection and localization of luggage trolleys under partial occlusion. The proposed system integrates the hierarchical process structure and progressive perception strategy. This innovative structure processes the luggage trolley’s position and orientation separately. It can accurately determine the luggage trolley’s position with just one well-detected keypoint and estimate its orientation when partially occluded. Once the luggage trolley’s initial pose is detected, the progressive perception strategy continuously refines this information until the robot begins grasping. The proposed system only needs RGB images for labeling and training, eliminating the need for complex data collection and annotation. The experiments on detection and localization demonstrate that the proposed system is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. © 2004-2012 IEEE.

关键词： Intelligent robots

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Introspective Perception for Long-Term Aerial Telemanipulation With Virtual Reality

Field Robotics

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Field robotics 2025年 3卷 323-367页

作者： Jongseok Lee Ribin Balachandran Konstantin Kondak Andre Coelho Marco De Stefano Matthias Humt Jianxiang Feng Tamim Asfour Rudolph Triebel Institute of Robotics and Mechatronics German Aerospace Center (DLR) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Robotics and Mechatronics Laboratory University of Twente (UT) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Chair of Computer Vision and Artificial Intelligence Technical University of Munich (TUM)

This paper presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot's workspace as well as a haptic guidance to its remotely located operator. To realize this, multiple sensors, namely, a LiDAR, cameras, and IMUs are utilized. For processing of the acquired sensory data, pose estimation pipelines are devised for industrial objects of both known and unknown geometries. We further propose an active learning pipeline in order to increase the sample efficiency of a pipeline component that relies on a Deep Neural Network (DNN) based object detector. All these algorithms jointly address various challenges encountered during the execution of perception tasks in industrial scenarios. In the experiments, exhaustive ablation studies are provided to validate the proposed pipelines. Methodologically, these results commonly suggest how an awareness of the algorithms' own failures and uncertainty (“introspection”) can be used to tackle the encountered problems. Moreover, outdoor experiments are conducted to evaluate the effectiveness of the overall system in enhancing aerial manipulation capabilities. In particular, with flight campaigns over days and nights, from spring to winter, and with different users and locations, we demonstrate over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM). As a result, we show the viability of the proposed system in future industrial applications. 1

关键词： Robots Service robots Sensors Pipelines Active learning Pose estimation Haptic interfaces Telepresence Visualization Virtual reality

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DualQuat-LOAM: LiDAR odometry and mapping parameterized on dual quaternions

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robotics and Autonomous Systems 2025年 191卷

作者： Velasco-Sánchez, Edison P. Recalde, Luis F. Li, Guanrui Candelas-Herias, Francisco A. Puente-Mendez, Santiago T. Torres-Medina, Fernando AUROVA Group of Automation Robotics and Computer Vision University of Alicante San Vicente del Raspeig S/N Alicante03690 Spain Worcester Polytechnic Institute Robotics Engineering Worcester01609 United States

This paper reports on a novel method for LiDAR odometry estimation, which completely parameterizes the system with dual quaternions. To accomplish this, the features derived from the point cloud, including edges, surfaces, and Stable Triangle Descriptor (STD), along with the optimization problem, are expressed in the dual quaternion set. This approach enables the direct combination of translation and orientation errors via dual quaternion operations, greatly enhancing pose estimation, as demonstrated in comparative experiments against other state-of-the-art methods. Our approach reduced drift error compared to other LiDAR-only-odometry methods, especially in scenarios with sharp curves and aggressive movements with large angular displacement. DualQuat-LOAM is benchmarked against several public datasets. In the KITTI dataset it has a translation and rotation error of 0.79% and 0.0039°/m, with an average run time of 53 ms. © 2025 The Author(s)

关键词： Parameterization

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ELK: Enhanced Learning Through Cross-Modal Knowledge Transfer for Lesion Detection in Limited-Sample Contrast-Enhanced Mammography Datasets 1st

ELK: Enhanced Learning Through Cross-Modal Knowledge Transfe...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Montoya-del-Angel, Ricardo Elbatel, Marawan Castillo-Lopez, Jorge Patricio Villaseñor-Navarro, Yolanda Brandan, Maria-Ester Marti, Robert Computer Vision and Robotics Institute University of Girona Girona Spain The Hong Kong University of Science and Technology Hong Kong Instituto Nacional de Cancerología Mexico City Mexico Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico

ISBN: (纸本)9783031777882

Contrast-enhanced mammography (CEM) offers improved breast cancer diagnosis by enhancing vascular contrast uptake. However, the development of reliable deep learning-based computer-aided detection (CAD) systems for CEM is hindered by limited data availability. This paper introduces ELK (Enhanced Learning through cross-modal Knowledge transfer), a deep learning pipeline designed to adapt large pre-trained models into a target limited data-volume population by leveraging synthetic data augmentation. Specifically, we adapt a detection model pretrained on digital breast tomosynthesis (DBT) and digital mammography data into a target CEM population using diffusion models to generate high-resolution, realistic synthetic lesions, preserving the visual integrity of CEM images. To assess the efficacy of our synthetic lesions, we compare the detection performance of a pretrained Faster R-CNN detector fine-tuned using only real images, synthetic images, and a combination of both. Our approach improves mean sensitivity by 4% on a test sample from the same population and by 7% on a newly collected out-of-domain CEM dataset. Our code and synthetic datasets are available at https://***/Likalto4/CEM-Detect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power Systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderly 2nd

Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Dis...

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2nd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the International Conference on Medical Image Computing and computer Assisted Interventions, MICCAI 2024

作者： Dang, Vien Ngoc Casamitjana, Adrià Hernández-González, Jerónimo Lekadir, Karim Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Research Institute of Computer Vision and Robotics University of Girona Girona Spain Departament d’Informàtica Matemàtica Aplicada i Estadística Universitat de Girona Girona Spain Barcelona Spain

ISBN: (纸本)9783031727863

Diagnosing Alzheimer’s disease (AD) presents significant challenges in the oldest populations due to overlapping symptoms of normal cognitive aging and early-stage dementia. While AI algorithms have matched specialist performance in diagnosing AD, they tend to produce unreliable results for the oldest populations, generating false positives that increase radiologist workloads and healthcare costs. In this study, we focus on mitigating overdiagnosis bias in CNN-based AD diagnosis for these groups. We present a post-hoc bias mitigation technique that significantly improves fairness by reducing overdiagnosis and enhances reliability by improving calibration without compromising overall model accuracy. Code is available at: https://***/ngoc-vien-dang/C-GTOP. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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*** 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

Field Robotics

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Field robotics 2025年 4卷 99-137页

作者： Ayoub Raji Danilo Caporale Francesco Gatti Andrea Giove Micaela Verucchi Davide Malatesta Nicola Musiu Alessandro Toschi Silviu Roberto Popitanu Fabio Bagni Massimiliano Bosi Alexander Liniger Marko Bertogna Daniele Morra Francesco Amerotti Luca Bartoli Federico Martello Riccardo Porta University of Modena and Reggio Emilia Italy University of Parma Italy Technology Innovation Institute-Autonomous Robotics Research Center UAE HIPERT srl University of Pisa Italy HIPERT srl Computer Vision Lab ETH Zurich Switzerland

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at an unprecedented speed and in a head-to-head scenario, using independently developed software on open-wheel race cars. This paper presents the complete software architecture used by the team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes, and reach speeds above 75m/s (270km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modeling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed second and third, respectively.

关键词： Automobiles Cameras Laser radar Autonomous vehicles Location awareness Safety Planning Vehicle dynamics Testing Sports Motion planning Environmental monitoring

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