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Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes

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Medical Image Analysis 2025年 103卷 103580页

作者： Manzano-Patrón, J.P. Deistler, Michael Schröder, Cornelius Kypraios, Theodore Gonçalves, Pedro J. Macke, Jakob H. Sotiropoulos, Stamatios N. Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham United Kingdom Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen & Tübingen AI Center Germany School of Mathematical Sciences University of Nottingham United Kingdom Belgium Department of Computer Science and Department of Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI. © 2025 The Authors

关键词： Monte Carlo methods

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GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping

arXiv

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arXiv 2025年

作者： Jose, Allen Isaac Pan, Sicong Zaenker, Tobias Menon, Rohit Houben, Sebastian Bennewitz, Maren Bonn-Rhein-Sieg University of Applied Sciences Germany Humanoid Robots Lab University of Bonn Germany Fraunhofer Institute for Intelligent Analysis and Information Systems Germany Humanoid Robots Lab University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to achieve adequate coverage or incur high robot motion costs. We introduce a global optimization approach for view motion planning that aims to minimize robot motion costs while maximizing fruit coverage. To this end, we leverage coverage constraints derived from the set covering problem (SCP) within a shortest Hamiltonian path problem (SHPP) formulation. While both SCP and SHPP are well-established, their tailored integration enables a unified framework that computes a global view path with minimized motion while ensuring full coverage of selected targets. Given the NP-hard nature of the problem, we employ a region-prior-based selection of coverage targets and a sparse graph structure to achieve effective optimization outcomes within a limited time. Experiments in simulation demonstrate that our method detects more fruits, enhances surface coverage, and achieves higher volume accuracy than the motion-efficient baseline with a moderate increase in motion cost, while significantly reducing motion costs compared to the coverage-focused baseline. Real-world experiments further confirm the practical applicability of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Fruits

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Preface

Cognitive Technologies

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Cognitive Technologies 2025年 Part F287卷 v-vi页

作者： Schulz, Daniel Bauckhage, Christian Research Center Machine Learning Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Sankt Nordrhein-Westfalen Augustin Germany Fraunhofer Institute for Intelligent Analysis and Information Systems Sankt Nordrhein-Westfalen Augustin Germany

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent

arXiv

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arXiv 2025年

作者： Liu, Zhiyu Han, Zhi Tang, Yandong Zhang, Hai Tang, Shaojie Wang, Yao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Statistics Northwest University Xi’an710000 China Department of Management Science and Systems State University of New York Buffalo United States Center for Intelligent Decision-making and Machine Learning School of Management Xi’an Jiaotong University Xi’an710049 China

We consider the noisy matrix sensing problem in the over-parameterization setting, where the estimated rank r is larger than the true rank r★. Specifically, our main objective is to recover a matrix X★ ∈ Rn1×n2 with rank r★ from noisy measurements using an over-parameterized factorized form LRT, where L ∈ n1×r, R ∈ n2×r and min{n1, n2} ≥ r > r★, with the true rank r★ being unknown. Recently, preconditioning methods have been proposed to accelerate the convergence of matrix sensing problem compared to vanilla gradient descent, incorporating preconditioning terms (LTL + λI)−1 and (RTR + λI)−1 into the original gradient. However, these methods require careful tuning of the damping parameter λ and are sensitive to initial points and step size. To address these limitations, we propose the alternating preconditioned gradient descent (APGD) algorithm, which alternately updates the two factor matrices, eliminating the need for the damping parameter and enabling faster convergence with larger step sizes. We theoretically prove that APGD achieves near-optimal error convergence at a linear rate, starting from arbitrary random initializations. Through extensive experiments, we validate our theoretical results and demonstrate that APGD outperforms other methods, achieving the fastest convergence rate. Notably, both our theoretical analysis and experimental results illustrate that APGD does not rely on the initialization procedure, making it more practical and versatile. Copyright © 2025, The Authors. All rights reserved.

关键词： Damping

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Self-Supervised learning of Scene-Graph Representations for Robotic Sequential Manipulation Planning 4

Self-Supervised Learning of Scene-Graph Representations for ...

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4th Conference on Robot learning, CoRL 2020

作者： Nguyen, Son-Tung Oguz, Ozgur S. Hartmann, Valentin N. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Learning and Intelligent Systems Group TU Berlin Germany

We present a self-supervised representation learning approach for visual reasoning and integrate it into a nonlinear program formulation for motion optimization to tackle sequential manipulation tasks. Such problems have usually been addressed by combined task and motion planning approaches, for which spatial relations and logical rules that rely on symbolic representations have to be predefined by the user. We propose to learn relational structures by leveraging visual perception to alleviate the resulting knowledge acquisition bottleneck. In particular, we learn constructing scene-graphs, that represent objects ("red box"), and their spatial relationships ("yellow cylinder on red box"). This representation allows us to plan high-level discrete decisions effectively using graph search algorithms. We integrate the visual reasoning module with a nonlinear optimization method for robot motion planning and verify its feasibility on the classic blocks-world domain. Our proposed framework successfully finds the sequence of actions and enables the robot to execute feasible motion plans to realize the given tasks. © 2020 Proceedings of machine learning Research. All rights reserved.

关键词： Motion planning

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RRGMambaFormer: A hybrid Transformer-Mamba architecture for radiology report generation

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Expert systems with Applications 2025年 279卷

作者： Li, Hongzhao Liu, Siwei Wang, Hui Jiang, Xiaoheng Jiu, Mingyuan Chen, Li Lu, Yang Li, Shupan Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University No. 100 Kexue Avenue Zhengzhou High-tech Industrial Development Zone Zhengzhou450001 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education No. 100 Kexue Avenue Zhengzhou High-tech Industrial Development Zone Zhengzhou450001 China National Supercomputing Center in Zhengzhou Changchun Road and Fengyang Street High-tech Zone Zhengzhou450001 China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

Radiology report generation (RRG) is a critical yet time-consuming task in clinical practice, requiring a high level of expertise to ensure accuracy. Automating this process using generative AI has the potential to significantly enhance efficiency and alleviate the burden on radiologists. In this paper, we introduce RRGMambaFormer, a novel hybrid architecture specifically designed for cross-modal radiology report generation that processes both medical images and text data. Our architecture synergistically combines Transformer modeling with Mamba sequence processing technology to handle the unique challenges of cross-modal medical data generation. This approach addresses key challenges in processing cross-modal medical data, including modality alignment, semantic gap bridging, and the computational inefficiency of traditional Transformer architectures. By replacing traditional positional encoding with a Mamba preprocessing block and incorporating an additional Mamba layer in the decoder, RRGMambaFormer enhances dynamic adaptability to both visual and textual modalities, improves sequence modeling, and reduces computational overhead. Furthermore, a multi-granularity contextual memory block is introduced to balance local visual details with global textual context, ensuring semantic consistency between image features and generated reports. Experimental results demonstrate that RRGMambaFormer not only improves the quality of radiology reports through better cross-modal feature integration but also significantly reduces computational complexity and inference time, achieving superior performance with fewer model parameters compared to existing Transformer-based models. Our source code is available here. © 2025 Elsevier Ltd

关键词： Radiology

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PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS 9

PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS

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9th International Conference on learning Representations, ICLR 2021

作者： Schubert, Ingmar Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Machine Learning and Robotics Lab University of Stuttgart Germany

In high-dimensional state spaces, the usefulness of Reinforcement learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict optimality guarantees of PB-RS to a guarantee of preserved long-term behavior. Being less restrictive, FV-RS allows for reward shaping functions that are even better suited for improving the sample efficiency of RL algorithms. In particular, we consider settings in which the agent has access to an approximate plan. Here, we use examples of simulated robotic manipulation tasks to demonstrate that plan-based FV-RS can indeed significantly improve the sample efficiency of RL over plan-based PB-RS. © 2021 ICLR 2021 - 9th International Conference on learning Representations. All rights reserved.

关键词： Reinforcement learning

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics 23

A Comparison of Prompt Engineering Techniques for Task Plann...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven Computer Science Institute Vi - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Autonomous Intelligent Systems group Germany

ISBN: (纸本)9798350373578

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt-Engineering. © 2024 IEEE.

关键词： Human robot interaction

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Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation 38

Sourcerer: Sample-based Maximum Entropy Source Distribution ...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Vetter, Julius Schröder, Cornelius Moss, Guy Gao, Richard Macke, Jakob H. Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen Germany Tübingen AI Center Germany Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations-an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible. Our method is purely sample-based-leveraging the Sliced-Wasserstein distance to measure the discrepancy between the dataset and simulations-and thus suitable for simulators with intractable likelihoods. We benchmark our method on several tasks, and show that it can recover source distributions with substantially higher entropy than recent source estimation methods, without sacrificing the fidelity of the simulations. Finally, to demonstrate the utility of our approach, we infer source distributions for parameters of the Hodgkin-Huxley model from experimental datasets with hundreds of single-neuron measurements. In summary, we propose a principled method for inferring source distributions of scientific simulator parameters while retaining as much uncertainty as possible. © 2024 Neural information processing systems foundation. All rights reserved.

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Towards cross-lingual automatic diagnosis of autism spectrum condition in Children's voices

Towards cross-lingual automatic diagnosis of autism spectrum...

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12. ITG-Fachtagung Sprachkommunikation - 12th ITG Conference on Speech Communication

作者： Schmitt, Maximilian Marchi, Erik Ringeval, Fabien Schuller, Björn Department of Complex and Intelligent Systems University of Passau Germany Machine Learning Group Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9783800742752

Automatic diagnosis of Autism Spectrum Conditions (ASC) from the voice is still in its infancy. The comparably few studies up to now focus mostly on the relevance of acoustic features and optimal learning algorithms. However, cross-lingual studies with a higher number of speakers are a white spot in the literature. The present contribution thus focusses on extensive cross-lingual evaluations based on four databases collected in English, French, Hebrew, and Swedish. The datasets contain speech of children with ASC and typically developing (TD) children matched in both age and gender. Their speech further intentionally varies in emotion. This introduces an additional challenge besides the change of languages. We demonstrate automatic ASC vs TD classification to be feasible despite such variation with a remaining error. © 2016 VDE VERLAG GMBH.

关键词： Diseases

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