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作者： Chen, Fang Liu, Jia Zhang, Xinran Liao, Hongen Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing210016 China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing10084 China

The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder–decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Forecasting

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Extraction of affine invariant features for shape recognition based on ant colony optimization

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Journal of Systems Engineering and Electronics 2011年第6期22卷 1003-1009页

作者： Yuxing Mao Ching Y. Suen Wei He State Key Laboratory of Power Transmission Equipment ＆ System Security and New Technology Chongqing University Chongqing 400044 E R. China Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal QC H3G 1M8 Canada

A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape ***,the centroid distance and azimuth angle of each boundary point are ***,with a prior-defined angle interval,all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating *** that,the centroid distance ratios（CDRs） of any two opposite contour points to the barycenter are achieved as the representation of the shape,which will be invariant to affine *** the angles of contour points will change non-linearly among affine related images,the CDRs should be resampled and combined sequentially to build one-by-one matching pairs of the corresponding *** core issue is how to determine the angle positions for sampling,which can be regarded as an optimization problem of path *** ant colony optimization（ACO）-based path planning model with some constraints is presented to address this ***,the Euclidean distance is adopted to evaluate the similarity of shape features in different *** experimental results demonstrate the efficiency of the proposed method in shape recognition with translation,scaling,rotation and distortion.

关键词： shape recognition affine transformation centroid distance ratio(CDR) ant colony optimization(ACO) path planning.

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Synthesizing Aβ-PET based on multi-modal neuroimaging fusion for pathological diagnosis of Alzheimer's disease

Synthesizing Aβ-PET based on multi-modal neuroimaging fusio...

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2024 International Conference on Cyber-Physical Social intelligence, ICCSI 2024

作者： Yang, Yueteng Li, Bing Li, Weikai Chen, Haifeng Cao, Wenming Chongqing Jiaotong University Department of Mathematics China Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Nanjing University Nanjing Drum Tower Hospital Affiliated Hospital of Medical School Department of Neurology Nanjing China

ISBN: (纸本)9798350376739

Synthesizing Aβ-PET images from cross-modal neuroimaging for diagnosing Alzheimer's disease through multi-modal medical image fusion is highly significant. However, there are relatively few studies in this area. We propose a multi-modal fusion adversarial method for Aβ-PET medical image synthesis, named MultiMed, to generate Aβ-PET images by fusing features from T1 and Flair images. Our approach employs an end-to-end method for image generation, incorporating a Multi-Feature Fusion Stream module for feature fusion. Integrating multimodal features enhances the model's representation capability and preliminarily designed a visual domain classifier for Alzheimer's disease diagnosis. We constructed an internal dataset for model training and validated our model on two external validation sets, achieving notable performance. © 2024 IEEE.

关键词： Neuroimaging

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ROBUST CROSS-SCENE FOREGROUND SEGMENTATION IN SURVEILLANCE VIDEO

ROBUST CROSS-SCENE FOREGROUND SEGMENTATION IN SURVEILLANCE V...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Liang, Dong Wei, Zongqi Sun, Han Zhou, Huiyu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization China School of Informatics University of Leicester United Kingdom

ISBN: (纸本)9781665438643

Training only one deep model for large-scale cross-scene video foreground segmentation is challenging due to the off-the-shelf deep learning based segmentor relies on scene-specific structural information. This results in deep models that are scene-biased and evaluations that are scene-influenced. In this paper, we integrate dual modalities (foregrounds' motion and appearance), and then eliminating features without representativeness of foreground through attention-module-guided selective-connection structures. It is in an end-to-end training manner and to achieve scene adaptation in the plug and play style. Experiments indicate the proposed method significantly outperforms the state-of-the-art deep models and background subtraction methods in untrained scenes - LIMU and LASIESTA. Source Code is available at: https://***/WeiZongqi/HOFAM. © 2021 IEEE

关键词： Optical flows

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A Double Regularization Loss Based on Long-tailed Noisy Labels

A Double Regularization Loss Based on Long-tailed Noisy Labe...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Lei Wang Shaoyuan Li MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

Extensive research has been conducted in recent years to solve the long-tailed distribution and achieved excellent results. However, in contrast to well-designed data, datasets with label noise are common in the real world, even in long-tailed datasets. Then, loss functions that rely on prior knowledge of correct labels for long-tailed distributions will fail. To solve the above problems, the robustness of different loss functions in long-tailed data containing noise is first analyzed. Then algorithmic improvements are made to the LDAM loss, which is focused on dealing with long-tailed problems. We propose a robust loss function DRL, which solves the noisy long-tailed problem with two regularisation terms: label regularisation and sample regularisation. Experiments on several datasets validate the effectiveness of the proposed loss function.

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Class-aware Learning for Imbalanced Multi-Label Classification

Class-aware Learning for Imbalanced Multi-Label Classificati...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Jiayao Chen Shaoyuan Li MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

Imbalanced multi-label image classification has gained increasing attention recently, in which each sample has multiple class labels, but the number of each category is unevenly distributed. It’s common in practical applications but traditional multi-label learning methods can hardly deal with imbalance problems. In this paper, we propose an effective method to tackle imbalanced multi-label learning. The class-aware embedding network is proposed to learn robust class-based representation. Additionally, by using the distribution-balanced loss to weigh different samples, our model can improve the feature learning ability of minority classes. Extensive experiments on widely used long-tailed manual multi-label datasets like VOC-LT and COCO-LT explicitly validate the proposed good method.

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Modular-Based Classifier for Phoneme Recognition

Modular-Based Classifier for Phoneme Recognition

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Abbas Ahmadi Fakhri Karray Mohamed Kamel Systems Design Engineering Department Pattern Analysis and Machine Intelligence Laboratory Waterloo ONT Canada Electrical and Computer Engineering Department Pattern Analysis and Machine Intelligence Laboratory Waterloo ONT Canada

This paper proposes a modular-based classifier for the problem of phoneme recognition. This is carried out by the use of a two-level classification approach including, high and low levels. We propose a new concept called phoneme family. To obtain phoneme families, we employ k-mean clustering method. A given unknown phoneme is first classified into a phoneme family at high level classification. Then, the exact label of the phoneme is determined at low level classification. We have used a combined framework of statistical and neural network based classifiers. Encouraging results are obtained by applying the proposed method on TIMIT database and its performance is compared against other methods

关键词： Neural networks Speech recognition Recurrent neural networks pattern recognition Design engineering Databases Network topology Signal processing Information technology pattern analysis

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Sharing training patterns in neural network ensembles

Sharing training patterns in neural network ensembles

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International Joint Conference on Neural Networks (IJCNN)

作者： R.A. Dara M. Kamel Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada

The need for the design of complex and incremental training algorithms in multiple neural network systems has motivated us to study combining methods from the cooperation perspective. One way of achieving effective cooperation is through sharing resources such as information and components. The degree and method by which multiple classifier systems share training resources can be a measure of cooperation. Despite the growing number of interests in data modification techniques, such as bagging and k-fold cross-validation, there is no guidance for whether sharing or not sharing training patterns results in higher accuracy and under what conditions. We implemented several partitioning techniques and examined the effect of sharing training patterns by varying the size of overlap between 0-100% of the size of training subsets. Under most conditions studied, multinet systems showed improvement over the presence of larger overlap subsets.

关键词： Intelligent networks Neural networks Bagging Boosting Diversity reception pattern analysis machine intelligence Laboratories Algorithm design and analysis Intelligent systems

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Multiple Cooperating Swarms for Data Clustering

Multiple Cooperating Swarms for Data Clustering

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IEEE Swarm intelligence Symposium, SIS

A new clustering technique by the use of multiple swarms is proposed. The proposed technique mimics the behavior of biological swarms which explore food situated in several places. We model the clustering problem using particle swarm optimization (PSO) approach. The proposed method considers multiple cooperating swarms to find centers of clusters. By assigning a portion of the solution space to each swarm, the exploration ability to find the solution is enhanced. Moreover, the cooperation among swarms increases the between-class distance. The proposed method outperforms k-means clustering as well as conventional PSO-based clustering techniques

关键词： Particle swarm optimization Data engineering Design engineering Biology computing Birds Educational institutions Marine animals Space exploration Equations pattern analysis

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An information-theoretic measure to evaluate data partitions in multiple classifiers

An information-theoretic measure to evaluate data partitions...

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IEEE International Conference on Systems, Man and Cybernetics

作者： R.A. Dara M. Makrehchi M. Kamel Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada

Data partitioning, such as bagging and boosting, has been extensively used in the construction of multiple classifier systems. One objective of data partitioning is to achieve uncorrelated classifiers. Most existing techniques achieve diversity through random partitioning, and they do not take advantage of the information within data patterns before training. In this work, combining techniques are studied and categorized from a new perspective. In addition, we introduce two new measures, total diversity index and imbalance, with which multiple classifiers can be compared. Several simulations and comparative studies have been carried out on a common benchmark data set and results are presented

关键词： Diversity reception Bagging machine intelligence Laboratories Information theory Design methodology pattern recognition Focusing Text categorization Image recognition

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