Isocitrate dehydrogenase (IDH) is a key molecular feature for gliomas, and the prediction of IDH is also an important task for computer-aided diagnosis using magnetic resonance imaging (MRI). To address this changllen...
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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 ea...
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Constrained clustering problems have been studied extensively in recent years. In this paper, we focus on a class of constrained k-median problems with general constraints on facilities, denoted as GCF k-CMedian probl...
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Unified information extraction (UIE) aims to extract diverse structured information from unstructured text. While large language models (LLMs) have shown promise for UIE, they require significant computational resourc...
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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...
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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.
Federated Learning (FL) presents a decentralized learning approach for privacy preservation. While many research focuses on uni-modal FL, a more intricate version, multimodal FL, uncovers fundamental attributes. Clien...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Federated Learning (FL) presents a decentralized learning approach for privacy preservation. While many research focuses on uni-modal FL, a more intricate version, multimodal FL, uncovers fundamental attributes. Clients might only gather specific modalities, incurring missing modalities in multi-modal FL. The missing modality problem incurs modality heterogeneity among clients and loses inter-modal connection within one client. To address these issues, we introduce a novel multi-modal FL method called Federated Missing Modality Reconstruction (FedMMR), which tackles these dual challenges through two distinct facets. First, we devise a cross-modal reconstruction policy for synthesizing absent modalities from other modalities. This aligns the data feature spaces across clients, thereby alleviating bias in resultant local models. Subsequently, we steer local models to maintain an inter-modal awareness of both existing and reconstructed modalities, recognizing their potential as complementary components. Comprehensive results show that FedMMR outperforms existing FL baselines.
Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) has been widely used in Mild Cognitive Impairment (MCI) detection. However, most existing methods rely on fixed sliding windows to capture the temporal var...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) has been widely used in Mild Cognitive Impairment (MCI) detection. However, most existing methods rely on fixed sliding windows to capture the temporal variations in brain connectivity and fail to model temporal features effectively. To address these limitations, we propose a spatio-temporal graph Transformer framework. First, we segment rs-fMRI data into distinct segments using the multivariate Gaussian distribution of brain region signals and remove redundant segments based on cumulative Jensen-Shannon divergence. Next, we extract features for each brain region and time point within segments, using these temporal features to guide spatial feature aggregation. A position encoder is designed to leverage temporal features across segments to capture both local and global spatio-temporal information. Finally, we employ a population graph framework as the classifier, with spatio-temporal features and demographic data of each subject forming the edge weights, to output the detection results. Our method achieves 92.80%, 81.89%, and 82.80% accuracy in MCI detection, early MCI detection, and late MCI detection tasks on the ADNI3 and ADNI2 datasets, respectively, outperforming existing methods.
Pathological question answering (PQA) is vital in computational pathology, as it involves interpreting pathological images and answering questions posed by humans. This interaction offers an effective means of engagin...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Pathological question answering (PQA) is vital in computational pathology, as it involves interpreting pathological images and answering questions posed by humans. This interaction offers an effective means of engaging with users and enhancing the understanding of pathology-related information. Recent methods developed using large vision-language models (LVLMs), such as QUILT-LLAVA, have made significant progress in advancing PQA. However, existing models, such as those using the QUILT-1M dataset, neglect the quality of the training set during the fine-tuning stage, leading to sub-optimal performance. We recognize that high-quality training data can significantly enhance model performance. Therefore, we design a model-based data filtering strategy to remove images with obvious impurities from the instruction fine-tuning dataset. The filtered high-quality images are then used to fine-tune the model. Additionally, most existing vision-language alignment strategies focus primarily on aligning local features through next-word prediction, leading to a relatively homogeneous granularity in inter-modal alignment. To address this issue, we propose a global-wise alignment module, which introduces global-level contrastive learning during the pretraining stage to establish multi-granularity alignment between pathological images and language descriptions. Based on the above two processes, we design our method, named Global Contrastive Learning with High-Quality Data (GCL-HQD) for pathological question answering in LVLMs. Extensive experiments on two types of experimental settings demonstrate the effectiveness of the GCL-HQD method.
STOchastic Recursive Momentum (STORM)based algorithms have been widely developed to solve one to K-level (K ≥ 3) stochastic optimization problems. Specifically, they use estimators to mitigate the biased gradient iss...
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STOchastic Recursive Momentum (STORM)based algorithms have been widely developed to solve one to K-level (K ≥ 3) stochastic optimization problems. Specifically, they use estimators to mitigate the biased gradient issue and achieve near-optimal convergence results. However, there is relatively little work on understanding their generalization performance, particularly evident during the transition from one to K-level optimization contexts. This paper provides a comprehensive generalization analysis of three representative STORM-based algorithms: STORM, COVER, and SVMR, for one, two, and K-level stochastic optimizations under both convex and strongly convex settings based on algorithmic stability. Firstly, we define stability for K-level optimizations and link it to generalization. Then, we detail the stability results for three prominent STORM-based algorithms. Finally, we derive their excess risk bounds by balancing stability results with optimization errors. Our theoretical results provide strong evidence to complete STORM-based algorithms: (1) Each estimator may decrease their stability due to variance with its estimation target. (2) Every additional level might escalate the generalization error, influenced by the stability and the variance between its cumulative stochastic gradient and the true gradient. (3) Increasing the batch size for the initial computation of estimators presents a favorable trade-off, enhancing the generalization performance. Copyright 2024 by the author(s)
With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added appl...
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