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Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1233-1247页

作者： Yiming Lei Haiping Zhu Junping Zhang Hongming Shan Shanghai Key Laboratory of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200433China with Huawei Shanghai 200120China Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433 Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 200031China IEEE

The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)***,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better *** improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning *** merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)***,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding ***,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction ***,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature *** training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian *** results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.

关键词： Terms-Convolutional neural network(CNNs) medical image classification meta-learning ordinal regression random forest

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EEG function network analysis of left and right hand motor imagery

EEG function network analysis of left and right hand motor i...

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2017 Chinese Automation Congress, CAC 2017

作者： Zhang, Rui Yan, Yamin Hu, Yuxia Shi, Li Wan, Hong Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electric Engineering Zhengzhou University Zhengzhou China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781538635247

Network analysis of signals originating from different parts of brain during motor imagery (MI) has gained lots of interest recently. In this paper, we used EEG to construct the brain network during MI, and analyzed the topological characteristics of the EEG function network. It is found that the node degree and clustering coefficient of the right hand MI is higher than the left hand MI on the nodes of right sensorimotor cortex, and the characteristic path length of the network is shorter for right hand MI than left hand MI. As for the right hand MI task, most subjects showed higher node degree and clustering coefficient on the right sensorimotor cortex than the left sensorimotor cortex. We conclude that EEG network based measures, which captures the brain information integrating characteristics during MI, may serve as useful features for classification in MI-BCI application, especially for BCI inefficiency subject. © 2017 IEEE.

关键词： Image analysis

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Language modulates vision: Evidence from neural networks and human brain-lesion models

arXiv

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arXiv 2025年

作者： Chen, Haoyang Liu, Bo Wang, Shuyue Wang, Xiaosha Han, Wenjuan Zhu, Yixing Wang, Xiaochun Bi, Yanchao State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain Research Beijing Normal University Beijing100875 China Department of Radiology First Hospital of Shanxi Medical University Shanxi Province Taiyuan030001 China College of Medical Imaging Shanxi Medical University Shanxi Province Taiyuan030001 China Shanxi Key Laboratory of Intelligent Imaging First Hospital of Shanxi Medical University Shanxi Province Taiyuan030001 China School of Computer Science & Technology Beijing Jiaotong University Beijing100044 China Institute for Artificial Intelligence Peking University Beijing100871 China State Key Laboratory of General Artificial Intelligence Peking University Beijing100871 China School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health Peking University Beijing100871 China IDG/McGovern Institute for Brain Research Peking University Beijing100871 China Chinese Institute for Brain Research Beijing102206 China Peking University Beijing100871 China

Comparing information structures in between deep neural networks (DNNs) and the human brain has become a key method for exploring their similarities and differences. Recent research has shown better alignment of vision-language DNN models, such as CLIP, with the activity of the human ventral occipitotemporal cortex (VOTC) than earlier vision models, supporting the idea that language modulates human visual perception. However, interpreting the results from such comparisons is inherently limited due to the "black box" nature of DNNs. To address this, we combined model–brain fitness analyses with human brain lesion data to examine how disrupting the communication pathway between the visual and language systems causally affects the ability of vision–language DNNs to explain the activity of the VOTC. Across four diverse datasets, CLIP consistently outperformed both label-supervised (ResNet) and unsupervised (MoCo) models in predicting VOTC activity. This advantage was left-lateralized, aligning with the human language network. Analyses of the data of 33 stroke patients revealed that reduced white matter integrity between the VOTC and the language region in the left angular gyrus was correlated with decreased CLIP performance and increased MoCo performance, indicating a dynamic influence of language processing on the activity of the VOTC. These findings support the integration of language modulation in neurocognitive models of human vision, reinforcing concepts from vision–language DNN models. The sensitivity of model–brain similarity to specific brain lesions demonstrates that leveraging manipulation of the human brain is a promising framework for evaluating and developing brain-like computer models. © 2025, CC BY.

关键词： brain models

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Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising

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Chinese Journal of Electronics 2014年第4期23卷 735-741页

作者： HOU Xin LI Jianwu LU Yao DONG Zhengchao Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and TechnologyBeijing Institute of Technology Brain Imaging Lab Columbia University

The patch matching of the traditional Nonlocal means(NLM) filter mainly depends on structure similarity and cannot adapt to the patch rotation or mirroring transformation. Therefore, designing a measure with rotationally invariant similarity is of significant importance for improving the effectiveness of patch comparison of NLM. This paper proposes to apply a no-reference image content metric with the rotation-invariance to NLM for denoising Magnetic resonance(MR) images. The metric measures quantitatively the content of a patch in an image, including sharpness, contrast, and geometric features such as textures and edges. The metric values for every patch are computed and added into the Gaussian matching kernel of NLM so as to effectively perform patch matching. The main advantage of the proposed method is that it does not need to rotate patches in different orientations during patch matching. Experimental results show that the proposed method is superior to the traditional NLM, the state-of-the-art method Block-matching and 3-D(BM3D) filtering and the Unbiased NLM(UNLM) for MRI denoising.

关键词： Nonlocal means Rotationally invariant similarity measure No-reference image content metric Magnetic resonance image denoising

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F2UE-NET: An Uncertainty-Driven Dynamic Expert Network for Long-Tail Retinal Disease Classification

F2UE-NET: An Uncertainty-Driven Dynamic Expert Network for L...

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IEEE International Symposium on Biomedical Imaging

作者： Junyong Zhao Tian Lin Meng Wang Liang Sun Haoyu Chen Daoqiang Zhang Huazhu Fu MOE Key Laboratory of Brain Computer Intelligence Technology College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Joint Shantou International Eye Center Shantou University and the Chinese University of Hong Kong Shantou Guangdong China Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Singapore

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The automatic diagnosis of retinal diseases presents a significant challenge in medical image analysis, primarily due to the long-tail distribution in existing retinal datasets. This imbalance creates substantial difficulties in model training and performance. To address this issue, we propose an uncertainty-driven dynamic expert network for long-tail retinal disease classification, called $\mathrm{F}^{2}\text{UE}$ -Net. Specifically, we apply Low-Rank Adaptation (LoRA) technology to fine-tune the foundation model, enhancing its feature extraction capabilities and overall performance. Additionally, we introduce an uncertainty-driven dynamic expert participation strategy, which assigns appropriate weights to different categories during training, ensuring high accuracy even for rare, tail-end diseases. Experimental results on a long-tail fundus disease dataset demonstrate that $\mathrm{F}^{2}$ UE outperforms state-of-the-art methods in long-tail retinal disease classification.

关键词： Training Heavily-tailed distribution Image analysis Foundation models Biological system modeling Tail Retina Feature extraction Medical diagnostic imaging Diseases

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SpikingMiniLM: energy-efficient spiking transformer for natural language understanding

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science China(Information sciences) 2024年第10期67卷 115-128页

作者： Jiayu ZHANG Jiangrong SHEN Zeke WANG Qinghai GUO Rui YAN Gang PAN Huajin TANG College of Computer Science and Technology Zhejiang University The State Key Lab of Brain-Machine Intelligence Zhejiang University Collaborative Innovation Center of Artificial Intelligence Zhejiang University Advanced Computing and Storage Laboratory Huawei Technologies Co. Ltd. College of Computer Science and Technology Zhejiang University of Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University

In the era of large-scale pretrained models, artificial neural networks（ANNs） have excelled in natural language understanding（NLU） tasks. However, their success often necessitates substantial computational resources and energy consumption. To address this, we explore the potential of spiking neural networks（SNNs） in NLU——a promising avenue with demonstrated advantages, including reduced power consumption and improved efficiency due to their event-driven characteristics. We propose the SpikingMiniLM,a novel spiking Transformer model tailored for natural language understanding. We first introduce a multi-step encoding method to convert text embeddings into spike trains. Subsequently, we redesign the attention mechanism and residual connections to make our model operate on the pure spike-based paradigm without any normalization technique. To facilitate stable and fast convergence, we propose a general parameter initialization method grounded in the stable firing rate principle. Furthermore, we apply an ANN-to-SNN knowledge distillation to overcome the challenges of pretraining SNNs. Our approach achieves a macro-average score of 75.5 on the dev sets of the GLUE benchmark, retaining 98% of the performance exhibited by the teacher model MiniLMv2. Our smaller model also achieves similar performance to BERTMINIwith fewer parameters and much lower energy consumption, underscoring its competitiveness and resource efficiency in NLU tasks.

关键词： spiking neural networks natural language understanding spiking Transformer spike-based attention multi-step encoding ANN-to-SNN distillation

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Study on neural information decoding of ground and flight motion state of pigeons in free-moving in the field 22

Study on neural information decoding of ground and flight mo...

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7th International Conference on Cyber Security and Information Engineering, ICCSIE 2022

作者： Ma, Erteng Yang, Lifang Shang, Zhigang School of Electrical Engineering Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain-Computer Interface Technology Zhengzhou China

ISBN: (纸本)9781450397414

The neural information decoding of birds in natural flight is very significant for the development of new biological robots, but there is little research at present. In this paper, we recorded the local field potential signals of pigeons under free movement in the wild;and constructed the brain function connectivity network underground and flight motion state by using the coherence between channels. The analysis shows that the information transmission of pigeons' brains is higher during flight, and its gamma-band contains more movement-related information. Finally, the clustering coefficients and global efficiency features of the gamma band are extracted for decoding. The average decoding accuracy of the three pigeons is 82.66%. This work can provide theoretical support for bird neural information decoding in the future natural environment. © 2022 ACM.

关键词： Birds

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An Adaptive Learning Rate Q-Learning Algorithm Based on Kalman Filter Inspired by Pigeon Pecking-Color Learning 14th

An Adaptive Learning Rate Q-Learning Algorithm Based on Kalm...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Li, Zhihui Shi, Li Yang, Lifang Shang, Zhigang Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical Engineering Zhengzhou University Zhengzhou450001 China Department of Automation Tsinghua University Beijing100000 China

ISBN: (纸本)9789811534140

The speed and accuracy of the Q-learning algorithm are critically affected by the learning rate. In most Q-learning application, the learning rate is usually set as a constant or decayed in a predetermined way, so it cannot meet the needs of dynamic and rapid learning. In this study, the learning process of pigeon pecking-color task was analyzed. We observed that there was epiphany phenomenon during pigeon’s learning process. The learning rate did not change gradually, but was large in the early stage and disappeared in the middle and late stage. Inspired by these phenomena, an adaptive learning rate Q-learning algorithm based on Kalman filter model (ALRKF Q-learning) is proposed in this paper. Q-learning are represented in the framework of Kalman filter model, and the learning rate is equivalent to Kalman gain, which dynamically weighs the fluctuation of environmental reward and the cognitive uncertainty of the agent to the value of pairs. The cognitive uncertainty in the model is determined by the variance of measurement residual and of environmental reward, and is set to zero when it is less than the variance of the environmental reward. The results tested by the two-armed Bandit task showed that the proposed algorithm not only can adaptively learn the statistical characteristics of environmental rewards, but also can quickly and accurately approximate the expected value of pairs. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Kalman filters

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Towards Calibrating Financial Market Simulators with High-frequency Data

arXiv

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arXiv 2025年

作者： Yang, Peng Ren, Junji Wang, Feng Tang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science Wuhan University Wuhan430072 China

The fidelity of financial market simulation is restricted by the so-called "non-identifiability" difficulty when calibrating high-frequency data. This paper first analyzes the inherent loss of data information in this difficulty, and proposes to use the Kolmogorov-Smirnov test (K-S) as the objective function for high-frequency calibration. Empirical studies verify that K-S has better identifiability of calibrating high-frequency data, while also leads to a much harder multi-modal landscape in the calibration space. To this end, we propose the adaptive stochastic ranking based negatively correlated search algorithm for improving the balance between exploration and exploitation. Experimental results on both simulated data and real market data demonstrate that the proposed method can obtain up to 36.0% improvement in high-frequency data calibration problems over the compared methods. Copyright © 2025, The Authors. All rights reserved.

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Neural Circuitry Involving Substance P in Male Sexual Behavior

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Neuroscience Bulletin 2024年第4期40卷 544-546页

作者： Xinrong Wang Junqiang Zheng Han Xu Department of Neurobiology and Department of Neurology of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-Computer Interface Institute Hangzhou311100China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-Machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Male sexual behavior is an innate social behavior crucial for reproduction[1].Previous studies have identified several brain regions crucial for the control of both sexual and aggressive behaviors in ***,molecularly specified neural circuits that specifically control male sexual behavior remain *** with the inherently rewarding nature of sexual behavior,previous research has implicated the role of the dopamine system in regulating sexual desire[2].However,the interaction between reward and copulation at the circuit level remains largely ***,it is unclear if the associated neural pathways are specific in modulating sexual reward.

关键词： behavior neural specified

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