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Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

Chinese Journal of Electronics 2021年第5期30卷 793-814页

作者： PAN Yi LIU Jin TIAN Xu LAN Wei GUO Rui Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South University School of Computer Electronics and Information Guangxi University

The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the indepth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size,irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently,some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally,objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.

关键词： biomedical MRI brain image segmentation machine learning methods brain hippocampal segmentation methods medical image processing neurophysiology related brain diseases diseases learning (artificial intelligence) hippocampus brain MRI images

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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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Domain-specific Knowledge Guided Self-supervised Learning for Pathological Image Segmentation

Domain-specific Knowledge Guided Self-supervised Learning fo...

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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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BEA-SegNet: Body and Edge Aware Network for Medical Image Segmentation

BEA-SegNet: Body and Edge Aware Network for Medical Image Se...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Kuang, Hulin Liang, Yixiong Liu, Ning Liu, Jin Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China

ISBN: (纸本)9781665401265

Medical image segmentation is a fundamental step for diagnosis and prognosis. This study proposes a new body and edge aware network for automated 2D medical image segmentation (called BEA-SegNet). The proposed BEA-SegNet consists of a shared encoder, a body and edge decouple (BEdecouple) module, two parallel decoders for body and edge segmentation. In the encoder and decoders, short-term multi-scale concatenation (STMSC) modules are utilized to implement multi-scale representation. We design a BEdecouple module to decouple the convolutional features into the body and edge features, making the proposed method be body and edge aware. The body and edge decoders utilize Bedecouple modules in each level to learn more effective features for the body and edge segmentation respectively, and their outputs are fused to generate the final segmentation. Besides, the body and edge supervision are applied to improve the final segmentation. The proposed BEA-SegNet is trained and evaluated on the International Skin Imaging Collaboration challenge 2018 dataset (ISIC2018). Experimental results show that the proposed BEA-SegNet achieves an average Dice similarity coefficient of 90.3% and an average Hausdorff distance of 15.9 for the skin lesion segmentation task and outperforms five benchmarks for skin lesion segmentation. © 2021 IEEE.

关键词： Convolutional neural networks

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MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction

MARE: Multi-Aspect Rationale Extractor on Unsupervised Ratio...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

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

ISBN: (纸本)9798891761643

Unsupervised rationale extraction aims to extract text snippets to support model predictions without explicit rationale annotation. Researchers have made many efforts to solve this task. Previous works often encode each aspect independently, which may limit their ability to capture meaningful internal correlations between aspects. While there has been significant work on mitigating spurious correlations, our approach focuses on leveraging the beneficial internal correlations to improve multi-aspect rationale extraction. In this paper, we propose a Multi-Aspect Rationale Extractor (MARE) to explain and predict multiple aspects simultaneously. Concretely, we propose a Multi-Aspect Multi-Head Attention (MAMHA) mechanism based on hard deletion to encode multiple text chunks simultaneously. Furthermore, multiple special tokens are prepended in front of the text with each corresponding to one certain aspect. Finally, multi-task training is deployed to reduce the training overhead. Experimental results on two unsupervised rationale extraction benchmarks show that MARE achieves state-of-the-art performance. Ablation studies further demonstrate the effectiveness of our method. Our codes have been available at https://***/CSU-NLP-Group/MARE. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention

ARSC-Net: Adventitious Respiratory Sound Classification Netw...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Xu, Lei Cheng, Jianhong Liu, Jin Kuang, Hulin Wu, Fan Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China

ISBN: (纸本)9781665401265

Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net), which combines residual block with channel-spatial attention for accurate classification. Specifically, we extract two types of features from adventitious respiratory sound, including Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-spectrogram. The two types of features are entered into the parallel encoders paths with residual attention for extracting feature representation, and then fused into a channel-spatial attention module to adaptively focus on the important features between channel and spatial part for the classification task. Moreover, the channel-spatial attention can enhance the feature representation, in which the channel attention explores the inter-channel relationship of the spectrums, and then the inter-spatial correlation mapping is generated by the spatial attention introduced serially. We evaluate our proposed method on ICBHI 2017 database. Experimental results show that our proposed method achieves encouraging predictive performance with an accuracy of 80.0% for identifying abnormal sounds from normal sounds, and with an accuracy of 92.4% for distinguishing crackles from wheezes. In addition, our method also achieves a score of 56.76% for the four-class sound classification of adventitious sounds and outperforms several state-of-the-art methods. © 2021 IEEE.

关键词： Automation

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FBC: Fusing Bi-Encoder and Cross-Encoder for Long-Form Text Matching 26

FBC: Fusing Bi-Encoder and Cross-Encoder for Long-Form Text ...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liao, Jianbo Jia, Mingyi Duan, Junwen Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University China

ISBN: (纸本)9781643684369

Semantic text matching has a wide range of applications in natural language processing. Recently proposed models that have achieved excellent results on short text matching tasks are not well suited to long-form text matching problems due to input length limitations and increased noise. On the other hand, long-form texts contain a large amount of information at different granularities after encoding, which cannot be fully interacted and utilized by existing methods. To address above issues, we propose a novel long-form text-matching framework which fuses Bi-Encoder and Cross-Encoder (FBC). Specially, it first employs an entity-driven key sentence extraction method to obtain the crucial content of the text and filter out noise. Subsequently, it integrates Bi-Encoder and Cross-Encoder to better capture semantic features and matching signals. Extensive experiments on several publicly available datasets demonstrate the effectiveness of our approach, compared with strong baselines. Furthermore, our model exhibits greater stability and accuracy in determining the matching relationship between documents describing the same event, which outperforms previously established approaches. The code is released at https://***/CSU-NLP-Group/FBC. © 2023 The Authors.

关键词： Semantics

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An integration of deep learning with feature fusion for protein-protein interaction prediction

An integration of deep learning with feature fusion for prot...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Tran, Hoai-Nhan Nguyen, Phuc-Xuan-Quynh Peng, Xiaoqing Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

High-throughput biological and large-scale experiments for protein-protein interaction (PPI) identification provides valuable information about PPI networks but are time-consuming and limited in determining PPI between different species. We propose the integration of deep learning with feature fusion for PPI prediction, combining handcrafted features and protein sequence embedding for protein sequence representation. Our proposed method is evaluated on the Yeast full, Yeast core, Human, and eight independent datasets. The experimental results show that our method achieves 95.8% accuracy on the Yeast core, 99.2% accuracy on Human, and 100% accuracy on eight independent datasets. We also perform extensive comparisons with other existing outstanding methods and demonstrate the superior ability of the proposed method. © 2022 IEEE.

关键词： Yeast

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ASNet: An Adversarial Sparse Network for Multi-task Biomedical Named Entity Recognition

ASNet: An Adversarial Sparse Network for Multi-task Biomedic...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Duan, Junwen Guo, Huai Zeng, Min Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

Biomedical named entity recognition (BioNER) is to extract entities, such as genes and proteins, from biomedical texts, where there is often a lack of high-quality training data. Recent work addresses this issue by multi-task learning with multiple datasets. However, these methods are usually over-parameterized, and some even suffer from negative transfer issues. To address above problems, we propose adversarial sparse sharing mechanism, which trains a sparsely shared encoder on multiple tasks with both task-agnostic and task-specific subnetworks. With adversarial training, we guide the task-agnostic subnetwork to learn shared task-invariant features and the task-specific subnetwork to learn task-dependent features. For a particular task, only the shared and its subnetwork are activated, which greatly reduces the number of parameters and avoids interference among tasks. Experimental results on 15 benchmark BioNER datasets show that our proposed method outperforms or is competitive with baseline methods with fewer parameters. Our code is released at: https://***/CSU-NLP-Group/ASNet © 2022 IEEE.

关键词： Natural language processing systems

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ViTRMSE: a three-dimensional RMSE scoring method for protein-ligand docking models based on Vision Transformer

ViTRMSE: a three-dimensional RMSE scoring method for protein...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Guo, Linyuan Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

Protein-ligand interactions (PLIs) play important roles in cellular activities and drug discovery. Due to the technical difficulty and high cost of experimental methods, there is considerable interest in the development of computational approaches, such as protein-ligand docking, to decipher PLI patterns. One of the most important and difficult aspects of protein-ligand docking is recognizing near-native conformations from a set of decoys, but unfortunately, traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. We present a new deep learning-based scoring function for ranking protein-ligand docking models based on Vision Transformer(ViT), named ViTRMSE. To recognize near-native conformations from a set of decoys, ViTRMSE voxelizes the protein-ligand interactional pocket into a 3D grid labeled by the occupancy contribution of atoms in different physicochemical classes. Benefiting from the Vision Transformer architecture, ViTRMSE can effectively capture the subtle differences between spatially and energetically favorable near-native conformations and unfavorable non-native decoys without needing extra information. ViTRMSE is extensively evaluated on diverse test sets including PDBbind2019 and CASF2016, and obtains significant improvements over existing methods in terms of RMSE, R and docking power. © 2022 IEEE.

关键词： Proteins

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