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Evaluating STU-Net for Brain Tumor Segmentation 1

引用

Challenge on Brain Tumor Segmentation, BraTS 2023, international Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for Computer Assisted Intervention conference, MICCAI 2023

作者： Huang, Ziyan Ye, Jin Wang, Haoyu Deng, Zhongying Su, Yanzhou Li, Tianbin Cheng, Junlong Chen, Jianpin Guo, Sizheng Shen, Yiqing He, Junjun Shanghai AI Laboratory Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Cambridge Cambridge United Kingdom

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Brain tumor segmentation is vital in addressing the tumor’s high heterogeneity, enhancing accurate diagnosis, guiding effective treatment, and improving prognosis predictions. In recent years, state-of-the-art methods in this domain have primarily evolved from U-Net-based architectures, demonstrating notable advancements in glioma radiographic segmentation. While large-scale models pre-trained on extensive datasets have significantly propelled deep learning progress, most current medical image segmentation models remain small-scale, encompassing only tens of millions of parameters. As the BraTS competition amasses more data and with the advancements of large-scale models in the past year, there emerges a compelling need to investigate the potential benefits of larger network architectures for adult glioma segmentation. In this context, we utilize the Scalable and Transferable U-Net (STU-Net) and its pre-trained variants on the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) 2023 dataset. Being one of the most extensive medical image segmentation models, STU-Net’s sizes range between 14 million to 1.4 billion parameters. Our research seeks to evaluate STU-Net’s efficacy and transferability on the BraTS23 dataset, encapsulating diverse MRI scans of brain tumor patients. The code and pre-trained models are available at https://***/uni-medical/STU-Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image segmentation

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Enhanced Parameter Identification of Fractional-Order PMSM in Robotic Joints Using Lévy Crayfish Optimization Algorithm

Enhanced Parameter Identification of Fractional-Order PMSM i...

引用

IEEE international conference on robotics and Biomimetics

作者： Hongjie Fan Hongxing Wei Dong Xu School of Mechanical Engineering and Automation Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The accuracy and response speed of torque in robotic joint models are crucial for enhancing the compliance of robot interactions. Consequently, parameter identification of the permanent magnet synchronous motor (PMSM) model in robotic joints has garnered significant attention from researchers world-wide. To further improve model accuracy, this study focuses on the parameter identification of a fractional-order model of PMSM. This paper introduces the Lévy Crayfish Optimization Algorithm (LCOA), an enhancement of the crayfish optimization algorithm (COA), designed to overcome COA's tendency to become trapped in local optima. The proposed algorithm employs the Lévy flights (LF) strategy to optimize the foraging stage, improving global search capabilities. As a result, the new algorithm exhibits superior stability and computational accuracy. Simulations were conducted to discuss the accuracy of parameter identification for the fractional-order PMSM.

关键词： Parameter estimation Accuracy Torque Computational modeling Metaheuristics Search problems Permanent magnet motors Stability analysis Particle swarm optimization Robots

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Generating Distinctive Marker Configurations for Robot Detection in Motion Capture Systems

Generating Distinctive Marker Configurations for Robot Detec...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Mahmoud Hamandi Abdullah Mohamed Ali Nikolaos Evangeliou Anthony Tzes Farshad Khorrami Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE Electrical and Computer Engineering New York University Brooklyn NY United States of America

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Motion Capture (MoCap) systems are commonly used to track indoor robots, providing measurements for robot control, ground truth data for testing new algorithms, and training datasets for learning-based methods. However, placing markers for MoCap tracking typically requires users to select distinct configurations for each robot while ensuring marker visibility and avoiding symmetric arrangements. This paper presents an optimization-based method for generating distinct marker configurations that ensure visibility, non-symmetry, and spatial distribution. The proposed approach automates the marker placement process, supporting both geometrically defined shapes and CAD model-based robot surfaces. The implementation is released as an open-source, user-friendly software tool, enabling streamlined marker placement for robotics applications.

关键词： Training Solid modeling Tracking Shape Motion capture Software tools Robots Surface treatment Principal component analysis Testing

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Needle in a Haystack: Finding Suitable Idioms Based on Text Descriptions 11th

Needle in a Haystack: Finding Suitable Idioms Based on Te...

引用

11th international conference on Analysis of Images, Social Networks and Texts, AIST 2023

作者： Zhernokleev, Dmitrii Braslavski, Pavel HSE University Moscow Russia Nazarbayev University Astana Kazakhstan

ISBN: (纸本)9783031545337

Idioms are an important part of natural languages and are often used to enhance expressiveness and fluency of speech. However, it can be difficult to find a contextually appropriate idiom when writing an essay or crafting a headline for a news article, especially for non-native speakers. This gives rise to the idea of an automated system that is able to recommend an idiom for an input sentence. The goal of this study is to develop and compare methods that would make such a system possible. We used an existing collection of idioms and employed several configurations based on models from the Sentence-BERT family. We also automatically expanded the initial dataset and fine-tuned a pre-trained Sentence-BERT model on the idiom/context matching task. This approach achieved the highest MRR score of 0.507. The data and the trained model are publicly available. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： automation

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Algorithms for Mathematical Remodeling and Generation of Training Sets

Algorithms for Mathematical Remodeling and Generation of Tra...

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international conference on Control Systems, Mathematical Modelling, automation and Energy Efficiency (SUMMA)

作者： Pavel Saraev Department of Automated Control Systems Lipetsk State Technical University Lipetsk Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

In this paper the problem of mathematical remodeling is considered. The conception of a new model building based on the already known model belonging to another class is described. It is shown that the central task is the generation of a training set, which can be produced in different ways. Algorithm for generation of data based on accounting for ranges of variation of the original function using interval estimation methods is proposed. The proposed approach consists of sequence of box bisections and the random points generation in proportion to the ranges of function range changes. The generalized mathematical remodeling algorithm is presented in the paper.

关键词： Training Analytical models Monte Carlo methods automation Computational modeling Buildings Estimation Control systems Mathematical models Energy efficiency

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Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives

Manipulation Planning Among Movable Obstacles Using Physics-...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Saxena, Dhruv Mauria Saleem, Muhammad Suhail Likhachev, Maxim Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781728190778

Robot manipulation in cluttered scenes often requires contact-rich interactions with objects. It can be more economical to interact via non-prehensile actions, for example, push through other objects to get to the desired grasp pose, instead of deliberate prehensile rearrangement of the scene. For each object in a scene, depending on its properties, the robot may or may not be allowed to make contact with, tilt, or topple it. To ensure that these constraints are satisfied during non-prehensile interactions, a planner can query a physicsbased simulator to evaluate the complex multi-body interactions caused by robot actions. Unfortunately, it is infeasible to query the simulator for thousands of actions that need to be evaluated in a typical planning problem as each simulation is timeconsuming. In this work, we show that (i) manipulation tasks (specifically pick-and-place style tasks from a tabletop or a refrigerator) can often be solved by restricting robot-object interactions to adaptive motion primitives in a plan, (ii) these actions can be incorporated as subgoals within a multi-heuristic search framework, and (iii) limiting interactions to these actions can help reduce the time spent querying the simulator during planning by up to 40x in comparison to baseline algorithms. Our algorithm is evaluated in simulation and in the real-world on a PR2 robot using PyBullet as our physics-based simulator. Supplementary video: https : //***/ABQc7JbeJPM.

关键词： Adaptation models Limiting automation Refrigerators conferences Search problems Planning

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Point Cloud models Improve Visual Robustness in Robotic Learners

Point Cloud Models Improve Visual Robustness in Robotic Lear...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Skand Peri Iain Lee Chanho Kim Li Fuxin Tucker Hermans Stefan Lee Oregon State University University of Utah NVIDIA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual control policies can encounter significant performance degradation when visual conditions like lighting or camera position differ from those seen during training – often exhibiting sharp declines in capability even for minor differences. In this work, we examine robustness to a suite of these types of visual changes for RGB-D and point cloud based visual control policies. To perform these experiments on both model-free and model-based reinforcement learners, we introduce a novel Point Cloud World Model (PCWM) and point cloud based control policies. Our experiments show that policies that explicitly encode point clouds are significantly more robust than their RGB-D counterparts. Further, we find our proposed PCWM significantly outperforms prior works in terms of sample efficiency during training. Taken together, these results suggest reasoning about the 3D scene through point clouds can improve performance, reduce learning time, and increase robustness for robotic learners. Code: https://***/pvskand/pcwm

关键词： Point cloud compression Training Degradation Visualization Three-dimensional displays Lighting Robustness

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Human-Aligned Longitudinal Control for Occluded Pedestrian Crossing With Visual Attention

Human-Aligned Longitudinal Control for Occluded Pedestrian C...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Vinal Asodia Zhenhua Feng Saber Fallah Department of Mechanical Engineering Sciences University of Surrey Guildford UK School of Computer Science and Electronic Engineering University of Surrey Guildford UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Reinforcement Learning (RL) has been widely used to create generalizable autonomous vehicles. However, they rely on fixed reward functions that struggle to balance values like safety and efficiency. How can autonomous vehicles balance different driving objectives and human values in a constantly changing environment? To bridge this gap, we propose an adaptive reward function that utilizes visual attention maps to detect pedestrians in the driving scene and dynamically switch between prioritizing safety or efficiency depending on the current observation. The visual attention map is used to provide spatial attention to the RL agent to boost the training efficiency of the pipeline. We evaluate the pipeline against variants of an occluded pedestrian crossing scenario in the CARLA Urban Driving simulator. Specifically, the proposed pipeline is compared against a modular setup that combines the well-established object detection model, YOLO, with a Proximal Policy Optimization (PPO) agent. The results indicate that the proposed approach can compete with the modular setup while yielding greater training efficiency. The trajectories collected with the approach confirm the effectiveness of the proposed adaptive reward function.

关键词： YOLO Training Visualization Adaptation models Pedestrians Pipelines Switches

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What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?

What Do We Learn from a Large-Scale Study of Pre-Trained Vis...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Sneha Silwal Karmesh Yadav Tingfan Wu Jay Vakil Arjun Majumdar Sergio Arnaud Claire Chen Vincent-Pierre Berges Dhruv Batra Aravind Rajeswaran Mrinal Kalakrishnan Franziska Meier Oleksandr Maksymets Meta AI Georgia Institute of Technology Stanford University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks. Our study involves five different PVRs, each trained for five distinct manipulation or indoor navigation tasks. We performed this evaluation using three different robots and two different policy learning paradigms. From this e ort, we can arrive at three insights: 1) the performance trends of PVRs in the simulation are generally indicative of their trends in the real world, 2) the use of PVRs enables a first-of-its-kind result with indoor ImageNav (zero-shot transfer to a held-out scene in the real world), and 3) the benefits from variations in PVRs, primarily data-augmentation and fine-tuning, also transfer to the real-world performance. See project website 1 for additional details and visuals.

关键词： Training Visualization Indoor navigation Market research Data augmentation Robot learning Data models

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Foggy Road Scene Detection Based on Improved Yolov8n

Foggy Road Scene Detection Based on Improved Yolov8n

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Ziqiang Shao Xiaohui Liu Lisheng Wei Tao Wu School of Electrical Engineering Anhui Polytechnic University Wuhu China Shanghai Oushuo Intelligent Packaging Technology Co. Ltd Shanghai China Anhui Polytechnic University Wuhu China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

In response to the target detection of intelligent systems under foggy conditions, which faces the problem of reduced detection accuracy due to reduced contrast of captured images, color shift and loss of details, a foggy road scene detection method based on improved Yolov8n is investigated. Firstly, the DehazeNet algorithm is combined with the target detection algorithm for defogging to obtain relatively clear images in order to reduce the interference of fog on target detection; Secondly, the Dilation-wise Residual (DWR) module is added to a portion of the C2f module of the backbone network, which enables the network to flexibly adapt to features at different scales to improve the detection accuracy; Finally, the proposed method is tested on the real fog-containing road scene dataset RTTS, and the mAP@50 is improved by 2 percentage points compared with the original model, which validates the feasibility and effectiveness of the design ideas and the proposed method in this paper.

关键词： Adaptation models Accuracy Image color analysis Roads Object detection Interference Feature extraction Intelligent systems Robots Faces

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