检索结果-内蒙古大学图书馆

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Gregor Koehler Tassilo Wald Constantin Ulrich David Zimmerer Paul F. Jaeger Jörg K. H. Franke Simon Kohl Fabian Isensee Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Helmholtz Imaging DKFZ National Center for Tumor Diseases (NCT) NCT Heidelberg a Partnership Between DKFZ University Medical Center Heidelberg Interactive Machine Learning Group DKFZ Machine Learning Lab University of Freiburg Freiburg Germany Latent Labs (***) London UK Applied Computer Vision Lab DKFZ Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Can a bifurcation diagram contain loops?

Can a bifurcation diagram contain loops?

引用

2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021

作者： Palshin, Gleb P. Ryabov, Pavel E. Sokolov, Sergei V. Financial University Department of Data Analysis and Machine Learning Moscow Russia Moscow Institute of Physics and Technology National Research University Department of Theoretical Mechanics Moscow Region Dolgoprudny Russia

ISBN: (纸本)9781665424066

The bifurcation diagram plays a major role in the study of the phase topology of completely Liouville-integrable Hamiltonian systems. In the works of A.T. Fomenko and A.V. Bolsinov, the problem of the permissible form of bifurcation diagrams is formulated. In particular, can a bifurcation diagram contain loops? The answer is positive. In this paper, the critical set of the integral mapping and the bifurcation diagram, which contains or almost contains a loop, is given by the example of the vortex dynamics problem. © 2021 IEEE.

关键词： Bifurcation (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding

arXiv

引用

arXiv 2023年

作者： Wu, Chengzhi Pfrommer, Julius Beyerer, Jürgen Li, Kangning Neubert, Boris Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer Center for Machine Learning Fraunhofer IOSB Karlsruhe Germany Institute for Visualization and Data Analysis Karlsruhe Institute of Technology Karlsruhe Germany

We present an improved approach for 3D object detection in point clouds data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed method considers the point neighborhood when computing point features. The newly introduced local neighborhood embedding operation mimics the convolutional operations in 2D neural networks. Thus features of each point are not only computed with the features of its own or of the whole point cloud, but also computed especially with respect to the features of its neighbors. Experiments show that our proposed method achieves better performance than the F-Pointnet baseline on 3D object detection tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification

On the Effectiveness of Heterogeneous Ensemble Methods for R...

引用

International Conference on machine learning and Applications (ICMLA)

作者： Simon Klüttermann Jérôme Rutinowski Frederik Polachowski Anh Nguyen Britta Grimme Moritz Roidl Emmanuel Müller TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Paderborn University Paderborn Germany Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods.

关键词： Training Accuracy Neural networks Metals Feature extraction Silicon Ensemble learning Security Reliability Pallets

来源：评论

学校读者我要写书评

暂无评论

PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images

arXiv

引用

arXiv 2024年

作者： Chen, Zifan Nan, Xinyu Li, Jiazheng Zhao, Jie Li, Haifeng Lin, Ziling Li, Haoshen Chen, Heyun Liu, Yiting Tang, Lei Zhang, Li Dong, Bin Center for Data Science Peking University Beijing China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

Background: Volumetric segmentation is crucial for medical imaging applications but faces significant challenges. Current approaches often require extensive manual annotations and scenario-specific model training, limiting their transferability across different tasks or modalities. While general segmentation models offer some versatility in natural image processing, they struggle with the unique characteristics of medical images. There is an urgent need in clinical practice for a new segmentation approach that can effectively handle medical imagery features while maintaining adaptability across various three-dimensional objects and imaging modalities. Methods: We introduce PAM (Propagating Anything Model), a propagation-based segmentation approach that operates on 3D medical image volumes using a 2D prompt (bounding box or sketch mask). PAM extrapolates this initial input to generate a complete 3D segmentation by modeling inter-slice structural relationships, establishing a continuous information flow within 3D medical structures. This approach enhances segmentation effectiveness across various imaging modalities by focusing on structural and semantic continuities rather than isolating specific objects. The model combines a CNN-based UNet architecture for intra-slice information processing with a Transformer-based attention module to facilitate inter-slice propagation. This innovative framework results in a method with unique generalizability, capable of segmenting diverse 3D objects across different medical imaging modalities. Results: PAM demonstrated superior performance on 44 diverse medical datasets, notably improving the dice similarity coefficient (DSC) for hundreds of segmentation object types and various medical imaging modalities. Compared to modern models like MedSAM and SegVol, PAM achieved an average DSC improvement of over 18.1%, while maintaining stable predictions despite prompt deviation (one-way ANOVA test, P ≥ 0.5985) and varying propagation confi

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

xMIL: Insightful Explanations for Multiple Instance learning in Histopathology 38

xMIL: Insightful Explanations for Multiple Instance Learning...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hense, Julius Idaji, Mina Jamshidi Eberle, Oliver Schnake, Thomas Dippel, Jonas Ciernik, Laure Buchstab, Oliver Mock, Andreas Klauschen, Frederick Müller, Klaus-Robert Berlin Institute for the Foundations of Learning and Data Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany Aignostics GmbH Berlin Germany Institute of Pathology Ludwig Maximilian University Munich Germany German Cancer Research Center Heidelberg Germany German Cancer Consortium Munich Germany Institute of Pathology Charité Universitätsmedizin Berlin Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of Max-Planck Institute for Informatics Saarbrücken Germany

Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognostication. However, MIL explanation methods are still lagging behind, as they are limited to small bag sizes or disregard instance interactions. We revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. We demonstrate how to obtain improved MIL explanations using layer-wise relevance propagation (LRP) and conduct extensive evaluation experiments on three toy settings and four real-world histopathology datasets. Our approach consistently outperforms previous explanation attempts with particularly improved faithfulness scores on challenging biomarker prediction tasks. Finally, we showcase how xMIL explanations enable pathologists to extract insights from MIL models, representing a significant advance for knowledge discovery and model debugging in digital histopathology. Codes are available at: https://***/bifold-pathomics/xMIL. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Generalization and Convergence by Enhancing Implicit Regularization

arXiv

引用

arXiv 2024年

作者： Wang, Mingze Wang, Jinbo He, Haotian Wang, Zilin Huang, Guanhua Xiong, Feiyu Li, Zhiyu Weinan, E. Wu, Lei School of Mathematical Sciences Peking University China Center for Machine Learning Research Peking University China China AI for Science Institute China School of Data Science University of Science and Technology of China China ByteDance Research China

In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence. Specifically, IRE decouples the dynamics of flat and sharp directions, which boosts the sharpness reduction along flat directions while maintaining the training stability in sharp directions. We show that IRE can be practically incorporated with generic base optimizers without introducing significant computational overload. Experiments show that IRE consistently improves the generalization performance for image classification tasks across a variety of benchmark datasets (CIFAR-10/100, ImageNet) and models (ResNets and ViTs). Surprisingly, IRE also achieves a 2× speed-up compared to AdamW in the pre-training of Llama models (of sizes ranging from 60M to 229M) on datasets including Wikitext-103, Minipile, and Openwebtext. Moreover, we provide theoretical guarantees, showing that IRE can substantially accelerate the convergence towards flat minima in sharpness-aware minimization (SAM). Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Using Convolutional Neural Network to Classify 2D EEG Scalp Topograms during Visual Task 5

Using Convolutional Neural Network to Classify 2D EEG Scalp ...

引用

5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Korchagin, Sergey Maksimenko, Vladimir Hramov, Alexander Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

we developed an artificial neural network (ANN) classifier to analyze the cortical activity signals during visual information processing. We tested several ANN architectures and chose a convolutional neural network (CNN) with the Resnet50 topology. As a result, the trained CNN classifier achieved an accuracy of 74% on the data of newly recruited subjects. © 2021 IEEE

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet machine learning Model 23

Stroke Home Rehabilitation Approach Using Mobile Application...

引用

7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

xMIL: insightful explanations for multiple instance learning in histopathology 24

xMIL: insightful explanations for multiple instance learning...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Julius Hense Mina Jamshidi Idaji Oliver Eberle Thomas Schnake Jonas Dippel Laure Ciernik Oliver Buchstab Andreas Mock Frederick Klauschen Klaus-Robert Müller Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany and Aignostics GmbH Berlin Germany Institute of Pathology Ludwig Maximilian University Munich Germany Institute of Pathology Ludwig Maximilian University Munich Germany and German Cancer Research Center Heidelberg and German Cancer Consortium Munich Germany Berlin Institute for the Foundations of Learning and Data Berlin Germany and Institute of Pathology Ludwig Maximilian University Munich Germany and German Cancer Research Center Heidelberg and German Cancer Consortium Munich Germany and Institute of Pathology Charité Universitätsmedizin Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany and Department of Artificial Intelligence Korea University Seoul Korea and Max-Planck Institute for Informatics Saarbrücken Germany

ISBN: (纸本)9798331314385

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：