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SeaConvNeXt:A Lightweight Two-Branch Network Architecture for Efficient Prediction of Specific IHC Proteins and Antigens on Hematoxylin and Eosin(H&E)Images

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Big Data Mining and Analytics 2024年第4期7卷 1212-1236页

作者： Yuli Chen Guoping Chen Guoying Shi Yao Zhou Jiayang Bai Germán Corredor Cheng Lu Xiujuan Lei School of Computer Science Shaanxi Normal UniversityXi’an 710119China Department of Pathology Shaanxi Provincial Hospital of Traditional Chinese MedicineXi’an 710003China Wallace H.Coulter Department of Biomedical Engineering Emory University and Georgia Institute of TechnologyAtlanta 30322GAUSAand also with Joseph Maxwell Cleland Atlanta VA Medical CenterDecatur 30033GAUSA Department of Radiology and Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou 510080Chinaand also with Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhou 510080China

Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and ***,IHC is costly and time-consuming,making it challenging to implement on a large *** address this issue,we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin(H&E)***,we have developed a novel registration technique,called Bi-stage Registration based on density Clustering(BiReC),to enhance the registration efficiency between H&E and IHC *** method involves automatically generating numerous Regions Of Interest(ROI)labels on the H&E image for model training,with the labels being determined by the intensity of IHC ***,we propose a novel two-branch network architecture,called SeaConvNeXt,which integrates a lightweight Squeeze-Enhanced Axial(SEA)attention mechanism to efficiently extract and fuse multi-level local and global features from H&E images for direct prediction of specific proteins and *** SeaConvNeXt consists of a ConvNeXt branch and a global fusion *** ConvNeXt branch extracts multi-level local features at four stages,while the global fusion branch,including an SEA Transformer module and three global blocks,is designed for global feature extraction and multiple feature *** experiments demonstrate that SeaConvNeXt outperforms current state-of-the-art methods on two public datasets with corresponding IHC and H&E images,achieving an AUC of 90.7%on the HER2SC dataset and 82.5%on the CRC *** results suggest that SeaConvNeXt has great potential for predicting virtual IHC staining on H&E images.

关键词： Immunohistochemistry(IHC) Bi-stage Registration based on density Clustering(BiReC) automatic label generation SeaConvNeXt attention mechanism multi-level local and global features virtual IHC staning prediction

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A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization

SSRN

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SSRN 2023年

作者： Cao, Jian Qian, Chen Huang, Yihui Chen, Dicheng Gao, Yuncheng Dong, Jiyang Guo, Di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Implicit regularization induced by gradient optimization is an important way to understand the generalization in neural networks. Recent theory explains implicit regularization over the model of deep matrix factorization (DMF) and analyzes the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics can mathematically characterize the practical learning rate of adaptive gradient optimization such as RMSProp. Discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization of RMSProp, i.e. landscape analysis. It mainly focuses on gradient regions like saddle points and local minima. We investigate that increasing learning rates benefit saddle point escaping (SPE) stages. In elucidating implicit regularization through the convergence of RMSProp, we prove that for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

arXiv

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arXiv 2022年

作者： Zhang, Kai Zhang, Kun Liu, Qi Huang, Zhenya Zhang, Mengdi Wu, Wei Cheng, Mingyue Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China Hefei China

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models. © 2022, CC BY.

关键词： Sentiment analysis

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Model-enhanced Vector Index

arXiv

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arXiv 2023年

作者： Zhang, Hailin Wang, Yujing Chen, Qi Chang, Ruiheng Zhang, Ting Miao, Ziming Hou, Yingyan Ding, Yang Miao, Xupeng Wang, Haonan Pang, Bochen Zhan, Yuefeng Sun, Hao Deng, Weiwei Zhang, Qi Yang, Fan Xie, Xing Yang, Mao Cui, Bin School of Computer Science Key Lab of High Confidence Software Technologies Peking University China Microsoft United States Aerospace Information Research Institute Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Carnegie Mellon University United States National University of Singapore Singapore National Engineering Laboratory for Big Data Analysis and Applications Peking University China

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with endto- end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Modelenhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embeddingbased models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Decomposed Neural Architecture Search for image denoising

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Applied Soft Computing 2022年 124卷

作者： Li, Di Bai, Yunpeng Bai, Zongwen Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an710129 China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University AberystwythSY233DB United Kingdom Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data School of Physics and Electronic Information Yan'an University Yan'an716000 China

In practical applications of deep learning, as the demand for the modeling capability increases, the network size may need to be massively enlarged in response. This may form a significant challenge in practice, especially when facing the dilemma of limited computational resources, making model compression indispensable. It can be time-consuming and interminable to obtain an appropriate network architecture through manual compression. In this paper, we propose an automated method for searching decomposed network architectures, named DNAS (standing for Decomposed Neural Architecture Search). It integrates both tasks of neural architecture search and tensor decomposition based model compression within a unified framework. The method is able to efficiently find a compact network with high performance for image denoising, with respect to memory and runtime. Particularly, using one single V100 GPU, it only takes about 1.5 h to obtain a denoising network on the BSD500 dataset. Experimental results demonstrate that compared with models developed using existing methods, DNAS consumes significantly less inference time and employs much fewer trainable parameters, outperforming existing approaches on both synthetic and real-world denoising datasets. © 2022 Elsevier B.V.

关键词： Network architecture

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Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios

arXiv

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arXiv 2025年

作者： Tang, Jiankai Liu, Jiacheng Tong, Renling Zhu, Kai Li, Zhe Yi, Xin Xing, Junliang Shi, Yuanchun Wang, Yuntao Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Ant Group China Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University China Ant Group China Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University China Intelligent Computing Application Laboratory of Qinghai Province Qinghai University China

Photoplethysmography (PPG) Sensors, widely deployed in smartwatches, offer a simple and non-invasive authentication approach for daily use. However, PPG authentication faces reliability issues due to motion artifacts from physical activity and physiological variability over time. To address these challenges, we propose MTL-RAPID, an efficient and reliable PPG authentication model, that employs a multitask joint training strategy, simultaneously assessing signal quality and verifying user identity. The joint optimization of these two tasks in MTL-RAPID results in a structure that outperforms models trained on individual tasks separately, achieving stronger performance with fewer parameters. In our comprehensive user studies regarding motion artifacts (N = 30), time variations (N = 32), and user preferences (N = 16), MTL-RAPID achieves a best AUC of 99.2% and an EER of 3.5%, outperforming existing baselines. We open-source our PPG authentication dataset along with the MTL-RAPID model to facilitate future research on GitHub. © 2025, CC BY.

关键词： Multi-task learning

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Quality meets diversity: A model-agnostic framework for computerized adaptive testing

arXiv

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arXiv 2021年

作者： Bi, Haoyang Ma, Haiping Huang, Zhenya Yin, Yu Liu, Qi Chen, Enhong Su, Yu Wang, Shijin Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China Anhui University China IFLYTEK Research

computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee’s knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity

关键词： Machine learning

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Consistency-aware Multi-modal Network for Hierarchical Multi-label Classification in Online Education System

Consistency-aware Multi-modal Network for Hierarchical Multi...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Siqi Lei Wei Huang Shiwei Tong Qi Liu Zhenya Huang Enhong Chen Yu Su Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China iFLYTEK Research iFLYTEK Co. Ltd

ISBN: (纸本)9781665438599

In the online education system, predicting the knowledge of exercises is a fundamental task of many applications, such as cognitive diagnosis. Usually, experts consider this problem as Hierarchical Multi-label Classification (HMC), since the knowledge concepts exhibit a multi-level structure. However, existing methods either sacrificed knowledge consistency for classification accuracy or sacrificed classification accuracy for knowledge consistency. Maintaining the balance is difficult. To forgo this dilemma, in this paper, we develop a novel frame-work called Consistency-Aware Multi-modal Network (Cam-Net). Specifically, we develop a multi-modal embedding module to learn the representation of the multi-modal exercise. Then, we adopt a hybrid prediction method consisting of the flat prediction module and the local prediction module. The local prediction module deals with the relation between the knowledge hierarchy and the input exercise. The flat prediction module focuses on maintaining knowledge consistency. Finally, to balance classification accuracy and knowledge consistency, we combine the outputs of two modules to make a final prediction. Extensive experimental results on two real-world datasets demonstrate the high performance and the ability to reduce knowledge inconsistency of CamNet.

关键词： Knowledge engineering Conferences Education Prediction methods Task analysis

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Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation for Better Domain Generalization

SSRN

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SSRN 2024年

作者： Wang, Hao Lin, Jiatai Li, Danyi Wang, Jing Zhao, Bingchao Shi, Zhenwei Liang, Changhong Han, Guoqiang Liang, Li Han, Chu Liu, Zaiyi The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China The Department of Pathology Nanfang Hospital Basic Medical College Southern Medical University Guangdong Guangzhou China Guangdong Province Key Laboratory of Molecular Tumor Pathology Guangdong Guangzhou China

Mitosis detection is one of the fundamental tasks in computational pathology, which is extremely challenging due to the heterogeneity of mitotic cell. Most of the current studies solve the heterogeneity in the technical aspect by increasing the model complexity. However, lacking consideration of the biological knowledge and the complex model design may lead to the overfitting problem while limited the generalizability of the detection model. In this paper, we systematically study the morphological appearances in different mitotic phases as well as the ambiguous non-mitotic cells and identify that balancing the data and feature diversity can achieve better generalizability. Based on this observation, we propose a novel generalizable framework (MitDet) for mitosis detection. The data diversity is considered by the proposed diversity-guided sample balancing (DGSB). And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP). Stain enhancement (SE) module is introduced to enhance the domain-relevant diversity of both data and features simultaneously. Extensive experiments have demonstrated that our proposed model outperforms all the state-of-the-art (SOTA) approaches in several popular mitosis detection datasets in both internal and unseen test sets using point annotations only. Comprehensive ablation studies have also proven the effectiveness of the rethinking of data and feature diversity balancing. By analyzing the results quantitatively and qualitatively, we believe that our proposed model not only achieves SOTA performance but also might inspire the future studies in new perspectives. © 2024, The Authors. All rights reserved.

关键词： Pathology

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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computer Systems Science & Engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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