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Standardized Transfer Learning models Enhance Classification of Breast Ultrasound Data 27

Standardized Transfer Learning Models Enhance Classification...

引用

27th international conference on System Theory, Control and Computing, ICSTCC 2023

作者： Bota, Maria Aurora Ciobotaru, Alexandru Bota, Peter Gota, Dan Ioan Stefan, Iulia Adina Valean, Honoriu Miclea, Liviu Automation Department Technical University of Cluj-Napoca Cluj-Napoca Romania Medical Department California University of Science and Medicine ColtonCA United States

ISBN: (纸本)9798350337983

Ultrasound imaging is an often employed technique in diagnosing of breast cancer, although the prediction reliability depends on the specialist's experience. Computer Aided Diagnosis (CAD) systems have been introduced for the enhancement the quality and time invested in classifying breast ultrasound(BUS) images. Deep Convolutional Neural Networks based algorithms is considered one of the most successful strategy in breast ultrasound image analysis. Data limitation is one of the prioritizing issues at the current moment. This problem is referred by introducing transfer-learning-based models and stratification as a data augmentation technique for achieving a better accuracy of the classification. The paper has demonstrated that the deep feature extraction and feature selection can categorized the breast ultrasound images using the pre-training methods. A dataset containing 1578 breast ultrasound images was used for model training and testing, and the optimal level of achievement was reached by InceptionResNetV2 and DenseNet121 with an accuracy of 83% and an"one over the rest" AUC score of 0.933 for DenseNet121, respectively 0.923 for InceptionResNetV2. © 2023 IEEE.

关键词： Convolutional neural networks

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Dark Reciprocal-Rank: Teacher-to-student Knowledge Transfer from Self-localization Model to Graph-convolutional Neural Network

Dark Reciprocal-Rank: Teacher-to-student Knowledge Transfer ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Koji, Takeda Kanji, Tanaka Univ Fukui Grad Sch Engn Fukui Japan Univ Fukui Fac Engn Fukui Japan

ISBN: (纸本)9781728190778

In visual robot self-localization, graph-based scene representation and matching have recently attracted research interest as robust and discriminative methods for self-localization. Although effective, their computational and storage costs do not scale well to large-size environments. To alleviate this problem, we formulate self-localization as a graph classification problem and attempt to use the graph convolutional neural network (GCN) as a graph classification engine. A straightforward approach is to use visual feature descriptors that are employed by state-of-the-art self-localization systems, directly as graph node features. However, their superior performance in the original self-localization system may not necessarily be replicated in GCN-based self-localization. To address this issue, we introduce a novel teacherto-student knowledge-transfer scheme based on rank matching, in which the reciprocal-rank vector output by an off-the-shelf state-of-the-art teacher self-localization model is used as the dark knowledge to transfer. Experiments indicate that the proposed graph-convolutional self-localization network (GCLN) can significantly outperform state-of-the-art self-localization systems, as well as the teacher classifier. The code and dataset are available at https ://***/Koj iTakeda00/Reciprocal_rank_KT_GCN.

关键词： Visualization Costs Codes automation conferences Neural networks Convolutional neural networks

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Automatic Generation of Logical Specifications for Behavioural models

Automatic Generation of Logical Specifications for Behaviour...

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IEEE/ACM international conference on Automated Software Engineering - Workshops (ASE Workshops)

作者： Radoslaw Klimek Julia Witek AGH University of Krakow Krakow Poland

ISBN: (数字)9798400712494

ISBN: (纸本)9798331523169

Logical specifications for behavioural models are crucial for the formal analysis of complex system designs. The automation of obtaining such a specification is essential particularly for promoting logical and deductive methods in software development. This article replicates earlier methods for automatically generating logical specifications equivalent to behavioural models, while also extending the approach to include workflow mining processes. Various and effective interactions with existing theorem provers are also proposed. We conducted straightforward, yet comprehensive, experiments covering multiple stages, which include workflow extraction, automatic logical specification generation, and theorem prover based analysis and the evaluation of these *** CONCEPTS• Theory of computation → Logic and verification, • Information systems → Information systems applications.

关键词： Analytical models automation Computational modeling conferences Data mining Logic Complex systems Information systems Software engineering Software development management

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6DOF Pose Estimation of a 3D Rigid Object Based on an Adaptive Model Curvature Point Pair Features

6DOF Pose Estimation of a 3D Rigid Object Based on an Adapti...

引用

international conference on Control, automation and robotics (ICCAR)

作者： Xin Zhao Xining Cui Hongyong Fu Technology and Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350373172

ISBN: (纸本)9798350373189

Currently, point cloud recognition algorithms that utilize the point pair feature (PPF) have demonstrated superior matching capabilities compared to their competitors even under conditions of occlusion and noise. However, including redundant point pairs in the global model description greatly diminishes the algorithm's efficiency. Insignificant scene reference points can readily result in incorrect matches. Additionally, different models require distinct parameter settings, making it challenging for new objects. Therefore, this paper focuses on the PPF algorithm and presents a 6D pose estimation method using an adaptive model curvature of Point Pair Features (AMC-PPF). The AMC-PPF algorithm is based on the PPF algorithm. Moreover, by utilizing adaptive truncation curvature, we reduce the unnecessary global model point pairs and select scene reference points that provide more geometric information. Furthermore, we adjust the sampling interval of scene reference points to maintain a consistent number of reference points, thereby improving accuracy. To verify the effectiveness of our method, we analyze the experimental results on the public dataset. The results demonstrate that the algorithm reduces redundant point pairs, it improves algorithm efficiency while ensuring algorithm accuracy.

关键词： Point cloud compression Adaptation models Solid modeling Matched filters Accuracy Three-dimensional displays Multithreading

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Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering

Learning Multimodal Contact-Rich Skills from Demonstrations ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Balakuntala, Mythra, V Kaur, Upinder Ma, Xin Wachs, Juan Voyles, Richard M. Purdue Univ Sch Engn Technol W Lafayette IN 47907 USA Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781728190778

Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand multimodal perception for effective and precise task execution. However, these present a novel challenge to robots as they lack the ability to combine these multimodal stimuli for performing contact-rich tasks. Learning-based methods have attempted to model multi-modal contact-rich tasks, but they often require extensive training examples and task-specific reward functions which limits their practicality and scope. Hence, we propose a generalizable model-free learning-from-demonstration framework for robots to learn contact-rich skills without explicit reward engineering. We present a novel multi-modal sensor data representation which improves the learning performance for contact-rich skills. We performed training and experiments using the real-life Sawyer robot for three everyday contact-rich skills - cleaning, writing, and peeling. Notably, the framework achieves a success rate of 100% for the peeling and writing skill, and 80% for the cleaning skill. Hence, this skill learning framework can be extended for learning other physical manipulation skills.

关键词： Training Learning systems automation Multimodal sensors conferences Writing Robot sensing systems

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Providing Automatic Feedback to Trainees after Automatic Evaluation

Providing Automatic Feedback to Trainees after Automatic Eva...

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IEEE international conference on robotics and automation (ICRA)

作者： Millan, Megane Achard, Catherine Sorbonne Univ CNRS UMR 7222 ISIR F-75005 Paris France

ISBN: (纸本)9781728190778

Learning how to perform precise and controlled gestures is difficult, especially when feedback about made errors is sparse. Therefore, some works try to facilitate learning by providing virtual "coaches". Most of them propose to automatically score task quality. But simply assessing quality through a score is not enough. Indeed, it is essential to provide explanations on assigned scores just like experts do when supervising trainees. However when quality assessment is done automatically, such explanations are rare and computing an automatic feedback is complex. In this work, we propose to address this problem by providing an automatic feedback based on neural network explanation. Contrary to previous state of the art methods, which are focused on neural networks explicability for classification tasks, we want to explain network decision on a regression problem (quality score prediction). Thus, we propose to use gradient-based approaches and adapt them to a regression task. Moreover, to address the problem of noise present in sensitivity maps, we propose a solution that leads to more robust gradients. To test our approach, since automatic quality assessment datasets do not contain ground truth about errors position and amplitude, a synthetic dataset representing a simple temporal task has been created, with its associated ground truth. Once the method has been validated on this synthetic dataset, we apply it on real data composed of robotic surgical gestures.

关键词： Sensitivity Correlation automation Databases conferences Artificial neural networks Robot sensing systems

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PENet: Towards Precise and Efficient Image Guided Depth Completion

PENet: Towards Precise and Efficient Image Guided Depth Comp...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Hu, Mu Wang, Shuling Li, Bin Ning, Shiyu Fan, Li Gong, Xiaojin Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Hisilicon Huawei Shanghai Dept Turing Solut Shanghai Peoples R China

ISBN: (纸本)9781728190778

Image guided depth completion is the task of generating a dense depth map from a sparse depth map and a high quality image. In this task, how to fuse the color and depth modalities plays an important role in achieving good performance. This paper proposes a two-branch backbone that consists of a color-dominant branch and a depth-dominant branch to exploit and fuse two modalities thoroughly. More specifically, one branch inputs a color image and a sparse depth map to predict a dense depth map. The other branch takes as inputs the sparse depth map and the previously predicted depth map, and outputs a dense depth map as well. The depth maps predicted from two branches are complimentary to each other and therefore they are adaptively fused. In addition, we also propose a simple geometric convolutional layer to encode 3D geometric cues. The geometric encoded backbone conducts the fusion of different modalities at multiple stages, leading to good depth completion results. We further implement a dilated and accelerated CSPN++ to refine the fused depth map efficiently. The proposed full model ranks 1st in the KITTI depth completion online leaderboard at the time of submission. It also infers much faster than most of the top ranked methods. The code of this work is available at https://***/JUGGHM/***2021.

关键词： Convolutional codes Three-dimensional displays automation Fuses Image color analysis conferences Color

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Target-Aware Implicit Mapping for Agricultural Crop Inspection

Target-Aware Implicit Mapping for Agricultural Crop Inspecti...

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IEEE international conference on robotics and automation (ICRA)

作者： Shane Kelly Alessandro Riccardi Elias Marks Federico Magistri Tiziano Guadagnino Margarita Chli Cyrill Stachniss Vision for Robotics Lab ETH Zurich University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany Department of Engineering Science University of Oxford UK

Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we exploit recent advancements in implicit mapping to tackle the challenging context of agricultural environments to create dense maps of crop rows with high enough fidelity to be useful for automated crop inspection. Specifically, we map strawberry and sweet pepper crop rows using RGB images captured by a wheeled mobile field robot inside a greenhouse and then use this data to build 3D maps to document the development of plants and fruits. Our Target-Aware Implicit Mapping system (TAIM) uses a SLAM-based pose initialization strategy for robust pose convergence, an efficient information-guided training sample selection framework for faster loss reduction, and focuses on exploiting training samples for fruit regions of the scene, which are critical for crop inspection tasks, to create more accurate maps in less time.

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Convergent iLQR for Safe Trajectory Planning and Control of Legged Robots

Convergent iLQR for Safe Trajectory Planning and Control of ...

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IEEE international conference on robotics and automation (ICRA)

作者： James Zhu J. Joe Payne Aaron M. Johnson Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In order to perform highly dynamic and agile maneuvers, legged robots typically spend time in underactuated domains (e.g. with feet off the ground) where the system has limited command of its acceleration and a constrained amount of time before transitioning to a new domain (e.g. foot touchdown). Meanwhile, these transitions can instantaneously change the system’s state, possibly causing perturbations to be mapped arbitrarily far away from the target trajectory. These properties make it difficult for local feedback controllers to effectively recover from disturbances as the system evolves through underactuated domains and hybrid impact events. To address this, we utilize the fundamental solution matrix that characterizes the evolution of perturbations through a hybrid trajectory and its 2-norm, which represents the worst-case growth of perturbations. In this paper, the worst-case perturbation analysis is used to explicitly reason about the tracking performance of a hybrid trajectory and is incorporated in an iLQR framework to optimize a trajectory while taking into account the closed-loop convergence of the trajectory under an LQR tracking controller. The generated convergent trajectories recover more effectively from perturbations, are more robust to large disturbances, and use less feedback control effort than trajectories generated with traditional methods.

关键词： Legged locomotion Trajectory planning Perturbation methods Simulation Robustness Trajectory Time factors

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Applications and Experiments on Query-based Object-aware Visual Language Mapping using Frame Division Method in Microcomputers

Applications and Experiments on Query-based Object-aware Vis...

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international conference on Control, automation and Information Sciences (ICCAIS)

作者： Jun Young Yun Daeyoel Kang Noheun Myeong Jongyoon Park Pileun Kim Department of Artificial Intelligence Korea Aerospace University Goyang Republic of Korea Department of Autonomous Vehicle Engineering Korea Aerospace University Goyang Republic of Korea

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

This paper demonstrates an approach to integrate object-aware map building technique which employs visual language models in microcomputers. This paper addresses the computational challenges of deploying visual language models in resource-constrained environments, such as mobile robots with microcomputers. It is achieved by separating object-aware map building process. The proposed mapping process is divided into 3 stages, data acquisition stage, object-aware map building stage, and inference stage. Experiments are conducted with Turtlebot4 mobile robot with Raspberry Pi microcomputers, to validate its performance in low-powered devices. The result showed 66% of success rate in overall text list, including 60% success rate with input texts with description. This study contributes to mobile robotics by showing that even microcomputers with limited processing power can support object-aware navigation tasks through optimized mapping method.

关键词： Performance evaluation Visualization automation Navigation Computational modeling Buildings Natural languages Data acquisition Microcomputers Mobile robots

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