检索结果-内蒙古大学图书馆

POS0900 AUTOMATIC SCORING OF EROSION, SYNOVITIS AND BONE OEDEMA IN RHEUMATOID ARTHRITIS USING DEEP LEARNING ON HAND MAGNETIC RESONANCE IMAGING

学校读者我要写书评

暂无评论

Annals of the Rheumatic Diseases 2023年 82卷 758-759页

作者： M. Schlereth A. Kleyer J. Utz L. Folle S. Bayat F. Fagni I. Minopoulou K. Tascilar J. Taubmann M. Uder T. Heimann J. Qiu G. Schett K. Breininger D. Simon Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany Department of Internal Medicine 3 - Rheumatology and Immunology Friedrich-Alexander Universität Erlangen-Nürnberg and University Hospital Erlangen Erlangen Germany Deutsches Zentrum Immuntherapie (DZI) Friedrich-Alexander Universität Erlangen-Nürnberg and University Hospital Erlangen Erlangen Germany Pattern Recognition Lab Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany Institute of Radiology Friedrich-Alexander Universität Erlangen-Nürnberg and University Hospital Erlangen Erlangen Germany Digital Technology and Innovation Siemens Healthcare GmbH Erlangen Germany

Background Rheumatoid Arthritis (RA) Magnetic Resonance Imaging (MRI) scoring system (RAMRIS) [1] is used to manually assess severity of disease activity and monitor treatment response, but it is dependent on observer variability and is time-consuming. Deep learning techniques have the potential to improve the reproducibility and efficiency of RAMRIS scoring by automating the analysis of hand MRI scans, however, there are limited data on an automated assessment approach. Objectives To investigate whether a deep neural network (DNN) can be trained to automatically detect erosion, synovitis, and oedema in RA patients using hand MRI scans and RAMRIS. Methods We used 1,5 Tesla hand MRI (Siemens Magnetom Vida and Aera) from the BARE BONE trial, a prospective, single-arm, interventional, open-label, phase 4 trial (EUDRACT 2018-001164-32) in which RA patients were treated with baricitinib (4 mg/day) for 48 weeks. One of the objectives of BARE BONE was to assess the effect of baricitinib on joint damage and synovial inflammation. Participants of BARE BONE received hand MRI at week 0, 24, 48 following a standardized scanning protocol [2]. All images were scored according to RAMRIS. DNNs were applied on coronal T1 (pre/post contrast enhancement) and T2 MR images. 3-D landmarks for each location for RAMRIS scoring were identified and a region of interest (ROI) around each landmark was extracted to train a DNN. Three separate DNNs were trained, one for each of the RAMRIS subcomponents (erosion, synovitis, oedema). Each DNN is based on a ResNet-3D [3] architecture that was pretrained on a video classification task [4]. The networks were trained to predict the severity scores of each disease characteristic into three classes ranging from 0 (no pathological change) to 2 (high disease burden). The performance of the DNNs was evaluated using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PR-AUC). Three-fold cross-val

关键词：

GPCA: A probabilistic framework for Gaussian process embedded channel attention

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Xie, Jiyang Ma, Zhanyu Chang, Dongliang Zhang, Guoqiang Guo, Jun Pattern Recognition and Intelligent System Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications China School of Electrical and Data Engineering University of Technology Sydney Australia

Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention modules have been proposed and implemented in various ways. Generally speaking, they are mainly based on convolution and pooling operations. In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way. The GPCA module intends to model the correlations among the channels, which are assumed to be captured by beta distributed variables. As the beta distribution cannot be integrated into the end-to-end training of convolutional neural networks (CNNs) with a mathematically tractable solution, we utilize an approximation of the beta distribution to solve this problem. To specify, we adapt a Sigmoid-Gaussian approximation, in which the Gaussian distributed variables are transferred into the interval [0, 1]. The Gaussian process is then utilized to model the correlations among different channels. In this case, a mathematically tractable solution is derived. The GPCA module can be efficiently implemented and integrated into the end-to-end training of the CNNs. Experimental results demonstrate the promising performance of the proposed GPCA module. Codes are available at https://***/PRIS-CV/GPCA. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Li, Yang Dong, Yiting Zhao, Dongcheng Zeng, Yi Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for biologically plausible spiking neural networks (SNNs). Datasets for traditional few-shot learning domains provide few amounts of temporal information. and the absence of neuromorphic datasets has hindered the development of few-shot learning for SNNs. Here, to the best of our knowledge, we provide the first neuromorphic dataset for few-shot learning using SNNs: N-Omniglot, based on the Dynamic Vision Sensor. It contains 1,623 categories of handwritten characters, with only 20 samples per class. N-Omniglot eliminates the need for a neuromorphic dataset for SNNs with high spareness and tremendous temporal coherence. Additionally, the dataset provides a powerful challenge and a suitable benchmark for developing SNNs algorithms in the few-shot learning domain due to the chronological information of strokes. We also provide the improved nearest neighbor, convolutional network, SiameseNet, and meta-learning algorithm in the spiking version for verification. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning algorithms

Effective and robust detection of adversarial examples via benford-fourier coefficients

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Ma, Chengcheng Wu, Baoyuan Xu, Shibiao Fan, Yanbo Zhang, Yong Zhang, Xiaopeng Li, Zhifeng School of Artificial Intelligence Chinese Academy of Sciences University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Tencent AI Lab China

Adversarial examples have been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i.e., shape factor, mean, and variance). GGD is a general distribution family to cover many popular distributions (e.g., Laplacian, Gaussian, or uniform). It is more likely to approximate the intrinsic distributions of internal responses than any specific distribution. Besides, since the shape factor is more robust to different databases rather than the other two parameters, we propose to construct discriminative features via the shape factor for adversarial detection, employing the magnitude of Benford-Fourier coefficients (MBF), which can be easily estimated using responses. Finally, a support vector machine is trained as the adversarial detector through leveraging the MBF features. Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust on detecting adversarial examples of different crafting methods and different sources, compared to state-of-the-art adversarial detection methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Fourier analysis

Multi-axis vision transformer for medical image segmentation

学校读者我要写书评

暂无评论

Engineering Applications of artificial intelligence 2025年 158卷

作者： Abdul Rehman Khan Asifullah Khan Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad 45650 Pakistan PIEAS Artificial Intelligence Center (PAIC) Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad 45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad 45650 Pakistan

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown remarkable success in medical image segmentation, but individually, they struggle to capture both local and global contexts. To address this limitation, we propose MaxViT-UNet, a hybrid encoder–decoder architecture that integrates convolutional operations and multi-axis self-attention to capture local and global context for effective medical image segmentation. Our novel Hybrid Decoder fuses upsampled decoder features with encoder skip connections and refines them using a multi-axis attention block, repeated across decoding stages for progressive segmentation refinement. Experimental evaluation on the MoNuSeg18 and MoNuSAC20 datasets demonstrates that MaxViT-UNet outperforms traditional CNN-based U-Net by 2.36% and 14.14% Dice score, respectively. Similarly it outperforms Swin-UNet by 5.31% on MoNuSeg18 and nearly doubles the Dice score on MoNuSAC20. These results confirm the generalization and effective segmentation capabilities of our hybrid architecture across diverse histopathological datasets.

关键词：

DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Possart, Dennis Mill, Leonid Vollnhals, Florian Hildebrand, Tor Suter, Peter Hoffmann, Mathis Utz, Jonas Augsburger, Daniel Thies, Mareike Wu, Mingxuan Wagner, Fabian Sarau, George Christiansen, Silke Breininger, Katharina Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-University Erlangen-Nürnberg Erlangen91052 Germany Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nürnberg Erlangen91058 Germany Institute for Nanotechnology and Correlative Microscopy Forchheim91301 Germany Lucid Concepts AG Zurich8005 Switzerland Correlative Microscopy and Materials Data Fraunhofer Institute for Ceramic Technologies and Systems Forchheim91301 Germany Institute of Experimental Physics Freie Universität Berlin Berlin91301 Germany Emeritus-Gruppe Leuchs Max Planck Institute for the Science of Light Erlangen91058 Germany Center for AI and Data Science Julius-Maximilians-University Würzburg Würzburg97074 Germany MIRA Vision Microscopy GmbH Göggingen73037 Germany

Nanomaterials exhibit distinctive properties governed by parameters such as size, shape, and surface characteristics, which critically influence their applications and interactions across technological, biological, and environmental contexts. Accurate quantification and understanding of these materials are essential for advancing research and innovation. In this regard, deep learning segmentation networks have emerged as powerful tools that enable automated insights and replace subjective methods with precise quantitative analysis. However, their efficacy depends on representative annotated datasets, which are challenging to obtain due to the costly imaging of nanoparticles and the labor-intensive nature of manual annotations. To overcome these limitations, we introduce DiffRenderGAN, a novel generative model designed to produce annotated synthetic data. By integrating a differentiable renderer into a Generative Adversarial Network (GAN) framework, DiffRenderGAN optimizes textural rendering parameters to generate realistic, annotated nanoparticle images from non-annotated real microscopy images. This approach reduces the need for manual intervention and enhances segmentation performance compared to existing synthetic data methods by generating diverse and realistic data. Tested on multiple ion and electron microscopy cases, including titanium dioxide (TiO2), silicon dioxide (SiO2), and silver nanowires (AgNW), DiffRenderGAN bridges the gap between synthetic and real data, advancing the quantification and understanding of complex nanomaterial systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

Learning Attentive Pairwise Interaction for Fine-Grained Classification

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhuang, Peiqin Wang, Yali Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Fine-grained classification is a challenging problem, due to subtle differences among highly-confused categories. Most approaches address this difficulty by learning discriminative representation of individual input image. On the other hand, humans can effectively identify contrastive clues by comparing image pairs. Inspired by this fact, this paper proposes a simple but effective Attentive Pairwise Interaction Network (API-Net), which can progressively recognize a pair of fine-grained images by interaction. Specifically, API-Net first learns a mutual feature vector to capture semantic differences in the input pair. It then compares this mutual vector with individual vectors to generate gates for each input image. These distinct gate vectors inherit mutual context on semantic differences, which allow API-Net to attentively capture contrastive clues by pairwise interaction between two images. Additionally, we train API-Net in an end-to-end manner with a score ranking regularization, which can further generalize API-Net by taking feature priorities into account. We conduct extensive experiments on five popular benchmarks in fine-grained classification. API-Net outperforms the recent SOTA methods, i.e., CUB-200-2011 (90.0%), Aircraft (93.9%), Stanford Cars (95.3%), Stanford Dogs (90.3%), and NABirds (88.1%). Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

Fast Texture Synthesis via Pseudo Optimizer

学校读者我要写书评

暂无评论

Fast Texture Synthesis via Pseudo Optimizer

Conference on Computer Vision and pattern recognition (CVPR)

作者： Wu Shi Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Texture synthesis using deep neural networks can generate high quality and diversified textures. However, it usually requires a heavy optimization process. The following works accelerate the process by using feed-forward networks, but at the cost of scalability. diversity or quality. We propose a new efficient method that aims to simulate the optimization process while retains most of the properties. Our method takes a noise image and the gradients from a descriptor network as inputs, and synthesize a refined image with respect to the target image. The proposed method can synthesize images with better quality and diversity than the other fast synthesis methods do. Moreover, our method trained on a large scale dataset can generalize to synthesize unseen textures.

关键词： Neural networks Training Transforms Scalability Optimization methods Convolution

Smallbignet: Integrating core and contextual views for video classification

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Li, Xianhang Wang, Yali Zhou, Zhipeng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Temporal convolution has been widely used for video classification. However, it is performed on spatio-temporal contexts in a limited view, which often weakens its capacity of learning video representation. To alleviate this problem, we propose a concise and novel SmallBig network, with the cooperation of small and big views. For the current time step, the small view branch is used to learn the core semantics, while the big view branch is used to capture the contextual semantics. Unlike traditional temporal convolution, the big view branch can provide the small view branch with the most activated video features from a broader 3D receptive field. Via aggregating such big-view contexts, the small view branch can learn more robust and discriminative spatio-temporal representations for video classification. Furthermore, we propose to share convolution in the small and big view branch, which improves model compactness as well as alleviates overfitting. As a result, our SmallBigNet achieves a comparable model size like 2D CNNs, while boosting accuracy like 3D CNNs. We conduct extensive experiments on the large-scale video benchmarks, e.g., Kinetics400, Something-Something V1 and V2. Our SmallBig network outperforms a number of recent state-of-the-art approaches, in terms of accuracy and/or efficiency. The codes and models will be available on https://***/xhl-video/SmallBigNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution