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Kernel based statistic: identifying topological differences in brain networks

Intelligent Medicine 2022年第1期2卷 30-40页

作者： Kai Ma Wei Shao Qi Zhu Daoqiang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsMIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjingJiangsu 210016China

Background Brain network describing interconnections between brain regions contains abundant topological *** is a challenge for the existing statistical methods(e.g.,t test)to investigate the topological differences of brain *** We proposed a kernel based statistic framework for identifying topological differences in brain *** our framework,the topological similarities between paired brain networks were measured by graph ***,graph kernels are embedded into maximum mean discrepancy for calculating kernel based test *** on this test statistic,we adopted conditional Monte Carlo simulation to compute the statistical significance(i.e.,P value)and statistical *** recruited 33 patients with Alzheimer’s disease(AD),33 patients with early mild cognitive impairment(EMCI),33 patients with late mild cognitive impairment(LMCI)and 33 normal controls(NC)in our *** are no statistical differences in demographic information between patients and *** compared state-of-the-art statistical methods include t test,t squared test,two-sample permutation test and non-normal *** We applied the proposed shortest path matched kernel to our framework for investigating the statistical differences of shortest path topological structures in brain networks of AD and *** compared our method with the existing state-of-the-art statistical methods in brain network characteristic including clustering coefficient and functional connection among EMCI,LMCI,AD,and *** results indicate that our framework can capture the statistically discriminative shortest path topological structures,such as shortest path from right rolandic operculum to right supplementary motor area(P=0.00314,statistical power=0.803).In clustering coefficient and functional connection,our framework outperforms the state-of-the-art statistical methods,such as P=0.0013 and statistical power=0.83 in the analysis of AD and *** Our proposed kernel b

关键词： Brain network Conditional monte carlo Graph kernel Statistical analysis Topological difference

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Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations 33

Causality-enhanced Discreted Physics-informed Neural Network...

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33rd International Joint Conference on Artificial intelligence, IJCAI 2024

作者： Li, Ye Chen, Siqi Shan, Bin Huang, Sheng-Jun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)9781956792041

Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficulties for evolutionary PDEs, particularly for dynamical systems whose solutions exhibit multi-scale or turbulent behavior over time. The reason is that PINNs may violate the temporal causality property since all the temporal features in the PINNs loss are trained simultaneously. This paper proposes to use implicit time differencing schemes to enforce temporal causality, and use transfer learning to sequentially update the PINNs in space as surrogates for PDE solutions in different time frames. The evolving PINNs are better able to capture the varying complexities of the evolutionary equations, while only requiring minor updates between adjacent time frames. Our method is theoretically proven to be convergent if the time step is small and each PINN in different time frames is well-trained. In addition, we provide state-of-the-art (SOTA) numerical results for a variety of benchmarks for which existing PINNs formulations may fail or be inefficient. We demonstrate that the proposed method improves the accuracy of PINNs approximation for evolutionary PDEs and improves efficiency by a factor of 4-40x. The code is available at https://***/SiqiChen9/TL-DPINNs. © 2024 International Joint Conferences on Artificial intelligence. All rights reserved.

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Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model

Probability analysis of axillary lymph node metastasis in br...

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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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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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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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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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Multi-label learning with pairwise relevance ordering 21

Multi-label learning with pairwise relevance ordering

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ming-Kun Xie Sheng-Jun Huang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106

ISBN: (纸本)9781713845393

Precisely annotating objects with multiple labels is costly and has become a critical bottleneck in real-world multi-label classification tasks. Instead, deciding the relative order of label pairs is obviously less laborious than collecting exact labels. However, the supervised information of pairwise relevance ordering is less informative than exact labels. It is thus an important challenge to effectively learn with such weak supervision. In this paper, we formalize this problem as a novel learning framework, called multi-label learning with pairwise relevance ordering (PRO). We show that the unbiased estimator of classification risk can be derived with a cost-sensitive loss only from PRO examples. Theoretically, we provide the estimation error bound for the proposed estimator and further prove that it is consistent with respect to the commonly used ranking loss. Empirical studies on multiple datasets and metrics validate the effectiveness of the proposed method.

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TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning

arXiv

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arXiv 2023年

作者： Liu, Jiexi Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence

Learning universal time series representations applicable to various types of downstream tasks is challenging but valuable in real applications. Recently, researchers have attempted to leverage the success of self-supervised contrastive learning (SSCL) in Computer Vision(CV) and Natural Language Processing(NLP) to tackle time series representation. Nevertheless, due to the special temporal characteristics, relying solely on empirical guidance from other domains may be ineffective for time series and difficult to adapt to multiple downstream tasks. To this end, we review three parts involved in SSCL including 1) designing augmentation methods for positive pairs, 2) constructing (hard) negative pairs, and 3) designing SSCL loss. For 1) and 2), we find that unsuitable positive and negative pair construction may introduce inappropriate inductive biases, which neither preserve temporal properties nor provide sufficient discriminative features. For 3), just exploring segment- or instance-level semantics information is not enough for learning universal representation. To remedy the above issues, we propose a novel self-supervised framework named TimesURL. Specifically, we first introduce a frequency-temporal-based augmentation to keep the temporal property unchanged. And then, we construct double Universums as a special kind of hard negative to guide better contrastive learning. Additionally, we introduce time reconstruction as a joint optimization objective with contrastive learning to capture both segment-level and instance-level information. As a result, TimesURL can learn high-quality universal representations and achieve state-of-the-art performance in 6 different downstream tasks, including short- and long-term forecasting, imputation, classification, anomaly detection and transfer learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

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Beyond myopia: learning from positive and unlabeled data through holistic predictive trends 23

Beyond myopia: learning from positive and unlabeled data thr...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xinrui Wang Wenhai Wan Chuanxing Geng Shaoyuan Li Songcan Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns. Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to 11.3% in key metrics. The code is available at https://***/wxr99/HolisticPU.

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