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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhui Kuang, Hulin Liu, Jin Li, Junjian Yue, Hailin Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China

ISBN: (纸本)9798350337488

Self-supervised learning provides a possible solution to extract effective visual representations from unlabeled pathological images. However, most of the existing methods either do not effectively utilize domain-specific information or are designed and optimized for image classification, resulting in these pre-trained models that may not be optimal for pathological image segmentation. In this paper, we propose DKSL: Domain-specific Knowledge guided Self-supervised Learning, which uses image reconstruction tasks to aid contrastive learning and exploits single-dye stained pathological images after stain separation as domain-specific knowledge to guide the model. Our method provides a novel way to exploit the domain-specific knowledge of pathological images. In contrastive learning, we add single-dye stained images as an expansion of the original positive samples to the contrastive learning process to preserve more global semantic information. In image reconstruction, the model is forced to focus on local image details relevant to downstream tasks by reconstructing single-dye stained images from the representation extracted by the encoder of contrastive learning. Finally, the encoder and decoder from the pre-training stage are fine-tuned by the downstream segmentation task. Fine-tuning experimental results demonstrate that DKSL outperforms state-of-the-art methods with Dices of 90.50% and 79.68% on two publicly available datasets, GlaS and MoNuSeg, respectively. © 2023 IEEE.

关键词： Contrastive learning Domain-specific knowledge Image reconstruction Pathological Image Segmentation

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A Comparison of Computational Approaches for Intron Retention Detection

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Big Data Mining and Analytics 2022年第1期5卷 15-31页

作者： Jiantao Zheng Cuixiang Lin Zhenpeng Wu Hong-Dong Li the Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

Intron Retention(IR)is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most *** has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex *** efforts have been dedicated to the development of IR detection *** methods differ in their metrics to quantify retention propensity,performance to detect IR events,functional enrichment of detected IRs,and computational speed.A systematic experimental comparison would be valuable to the selection and use of existing *** this work,we conduct an experimental comparison of existing IR detection *** the unavailability of a gold standard dataset of intron retention,we compare the IR detection performance on simulation ***,we compare the IR detection results with real RNA-Seq *** also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment,which is illustrated on an Alzheimer’s disease RNA-Seq *** discuss key principles and features of existing approaches and outline their *** systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.

关键词： alternative splicing intron retention gene expression RNA-Seq

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Improving Medical Image Denoising via a Lightweight Plug-and-play Module

Improving Medical Image Denoising via a Lightweight Plug-and...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Ma, Lei Kuang, Hulin Liu, Jin Shen, Chengchao Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Medical image denoising, as a part of medical image processing, is significant for the assessment and diagnosis of diseases. To improve the medical image denoising performance of existing deep learning methods, we propose a lightweight plug-and-play module (LP2M) with low complexity, which can be plugged before current image denoising methods. Specifically, the proposed LP2M contains three stacked Convolutional Neural Network (CNN) based blocks: a image receptor block, an adaptive receptive field selection block and a high-low frequency processing block. The image receptor block can perceive color or grayscale images and perform preliminary processing. The adaptive receptive field selection block includes two parallel paths with different receptive fields (i.e., convolution kernel sizes) and the adaptive weighting operation, which can process multi-scale information in the image. The high-low frequency processing block consists of a low frequency pathway using convolutional layers with large kernel sizes, and a high frequency pathway using covolutional layers with small kernel sizes, which can process the low and high frequency components in images. Extensive validation experiments are performed on five state-of-the-art denoising methods on multiple medical image datasets for three different medical image denoising tasks (X-ray image denoising, magnetic resonance image denoising and dermoscopic image denoising). Experimental results show that our proposed LP2M can effectively improve the results of these five state-of-the-art methods for three denoising tasks with only increasing 0.996K parameters and 63.112M FLOPs, and it can provide potential direction for improving image denoising. © 2023 IEEE.

关键词： adaptive receptive field selection high-low frequency processing image receptor medical image denoising plug-and-play

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PathActMarker:an R package for inferring pathway activity of complex diseases

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Frontiers of computer science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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Personalized federated adaptive regularization for heterogeneous medical image classification: COVID-19 CT results

Personalized federated adaptive regularization for heterogen...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yuan Qu, Zhe Wang, Shu Sheng, Tao Yu, Xiaoqing Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China Bank of Sanxiang Hunan Changsha China

ISBN: (纸本)9798350337488

During outbreaks of large infectious diseases like COVID-19, there is a strain on healthcare resources worldwide. To alleviate the burden on healthcare workers during the initial stages of the outbreak, there is an urgent need for the development of automated tools. Federated learning offers a privacypreserving solution to the challenge that limited annotated data within single healthcare facilities. However, existing federated aggregation strategies cannot adapt to the real-world medical image data problem of mixed heterogeneous. This paper introduces Personalized Federated Adaptive Regularization (pFedAR), an adaptive framework for federated learning that effectively utilizes multi-site COVID-19 CT datasets with mixed heterogeneous of distribution discrepancies. To solve the mixed distribution of label skew and feature shift, we propose a two-stage adaptive regularization. In the client training stage, we use the balance loss term to balance the COVID-19 clients with missing label. In the aggregation stage, we correct the client gradient conflict. Our method is developed and evaluated using eight real-world COVID-19 diagnosis datasets composed of CT images. Extensive experiments demonstrate the consistent improvement achieved by our method across the datasets. © 2023 IEEE.

关键词： federated learning heterogeneity medical image personalization

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MHLAT: Multi-Hop label-Wise Attention Model for Automatic ICD Coding 48

MHLAT: Multi-Hop Label-Wise Attention Model for Automatic IC...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Duan, Junwen Jiang, Han Yu, Ying Central South University School of Computer Science and Engineering Hunan Changsha China Central South University Hunan Provincial Key Lab on Bioinformatics Hunan Changsha China

ISBN: (纸本)9781728163277

International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000 tokens). However, unlike the single-pass reading process in previous works, humans tend to read the text and label definitions again to get more confident answers. Moreover, although pretrained language models have been used to address these problems, they suffer from huge memory usage. To address the above problems, we propose a simple but effective model called the Multi-Hop label-wise ATtention (MHLAT), in which multi-hop label-wise attention is deployed to get more precise and informative representations. Extensive experiments on three benchmark MIMIC datasets indicate that our method achieves significantly better or competitive performance on all seven metrics, with much fewer parameters to optimize. © 2023 IEEE.

关键词： bias-terms fine-tuning ICD coding multi-hop label-wise attention pre-trained language model

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Intelligent Electrocardiogram Analysis in Medicine:Data,Methods,and Applications

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Chinese Medical sciences Journal 2023年第1期38卷 38-48页

作者： Yu-Xia Guan Ying An Feng-Yi Guo Wei-Bai Pan Jian-Xin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive *** can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire ***,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric *** recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis *** we systematically summarize and analyze the ECGbased automatic analysis methods and ***,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing *** we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these ***,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

关键词： Electrocardiogram database preprocessing machine learning medical big data analysis

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KL-RF: Predicting disease-gene associations with model fusion

KL-RF: Predicting disease-gene associations with model fusio...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Hai Li, Hong-Dong Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan ChangSha China

ISBN: (纸本)9781665468190

Appropriate disease gene prediction methods are not only critical for the diagnosis of many genotypic diseases, but well-positioned to help scientists discover the links between complex diseases and disease-causing genes. As the development of high-throughput biotechnology, a great quantity of biomolecular interaction networks and omics data emerge rapidly. Because of the capacity of biomolecular networks to indicate complex interactions between elements in disease, they are one of the most frequently used data in the field of disease-gene associations prediction. Fusing these biomolecular networks effectively is hopeful to improve the performance of computational methods. Here, we develop a method to predict the associations between disease and gene by fusing the information of heterogeneous datasets. This method first learns the latent feature representation from disease-gene similarity networks via graph representation learning methods, then utilizes a Beyesian approach to obtain the posterior distribution of the feature matrix, and subsequently fuses the posterior distribution based on KL divergence and predicts the associations by random forest (KL-RF) finally. We compared KL-RF with several classic methods, and the results demonstrate that it performs better than the existing methods. Further analysis of the top-10 unknown disease genes and case study also reveals the ability of the proposed method. The source codes is available on https://***/genemine/KL-RF. © 2022 IEEE.

关键词： Association reactions

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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