arXiv subjects

Jiaqing Zhang

Publications and source records attributed to Jiaqing Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2024

DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR

Delirium is an acute confusional state that has been shown to affect up to 31% of patients in the intensive care unit (ICU). Early detection of this condition could lead to more timely interventions and improved health outcomes. While artificial intelligence (AI) models have shown great potential for ICU delirium prediction using structured electronic health records (EHR), most of them have not explored the use of state-of-the-art AI models, have been limited to single hospitals, or have been developed and validated on small cohorts. The use of large language models (LLM), models with hundreds of millions to billions of parameters, with structured EHR data could potentially lead to improved predictive performance. In this study, we propose DeLLiriuM, a novel LLM-based delirium prediction model using EHR data available in the first 24 hours of ICU admission to predict the probability of a patient developing delirium during the rest of their ICU admission. We develop and validate DeLLiriuM on ICU admissions from 104,303 patients pertaining to 195 hospitals across three large databases: the eICU Collaborative Research Database, the Medical Information Mart for Intensive Care (MIMIC)-IV, and the University of Florida Health's Integrated Data Repository. The performance measured by the area under the receiver operating characteristic curve (AUROC) showed that DeLLiriuM outperformed all baselines in two external validation sets, with 0.77 (95% confidence interval 0.76-0.78) and 0.84 (95% confidence interval 0.83-0.85) across 77,543 patients spanning 194 hospitals. To the best of our knowledge, DeLLiriuM is the first LLM-based delirium prediction tool for the ICU based on structured EHR data, outperforming deep learning baselines which employ structured features and can provide helpful information to clinicians for timely interventions.

cs.AI↗

arXiv2024

SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection

Multimodal object detection leverages diverse modal information to enhance the accuracy and robustness of detectors. By learning long-term dependencies, Transformer can effectively integrate multimodal features in the feature extraction stage, which greatly improves the performance of multimodal object detection. However, current methods merely stack Transformer-guided fusion techniques without exploring their capability to extract features at various depth layers of network, thus limiting the improvements in detection performance. In this paper, we introduce an accurate and efficient object detection method named SeaDATE. Initially, we propose a novel dual attention Feature Fusion (DTF) module that, under Transformer's guidance, integrates local and global information through a dual attention mechanism, strengthening the fusion of modal features from orthogonal perspectives using spatial and channel tokens. Meanwhile, our theoretical analysis and empirical validation demonstrate that the Transformer-guided fusion method, treating images as sequences of pixels for fusion, performs better on shallow features' detail information compared to deep semantic information. To address this, we designed a contrastive learning (CL) module aimed at learning features of multimodal samples, remedying the shortcomings of Transformer-guided fusion in extracting deep semantic features, and effectively utilizing cross-modal information. Extensive experiments and ablation studies on the FLIR, LLVIP, and M3FD datasets have proven our method to be effective, achieving state-of-the-art detection performance.

cs.CV↗

arXiv2024

Multi-scale direction-aware SAR object detection network via global information fusion

Deep learning has driven significant progress in object detection using Synthetic Aperture Radar (SAR) imagery. Existing methods, while achieving promising results, often struggle to effectively integrate local and global information, particularly direction-aware features. This paper proposes SAR-Net, a novel framework specifically designed for global fusion of direction-aware information in SAR object detection. SAR-Net leverages two key innovations: the Unity Compensation Mechanism (UCM) and the Direction-aware Attention Module (DAM). UCM facilitates the establishment of complementary relationships among features across different scales, enabling efficient global information fusion and transmission. Additionally, DAM, through bidirectional attention polymerization, captures direction-aware information, effectively eliminating background interference. Extensive experiments demonstrate the effectiveness of SAR-Net, achieving state-of-the-art results on aircraft (SAR-AIRcraft-1.0) and ship datasets (SSDD, HRSID), confirming its generalization capability and robustness.

cs.CV↗

arXiv2024

FoRA: Low-Rank Adaptation Model beyond Multimodal Siamese Network

Multimodal object detection offers a promising prospect to facilitate robust detection in various visual conditions. However, existing two-stream backbone networks are challenged by complex fusion and substantial parameter increments. This is primarily due to large data distribution biases of multimodal homogeneous information. In this paper, we propose a novel multimodal object detector, named Low-rank Modal Adaptors (LMA) with a shared backbone. The shared parameters enhance the consistency of homogeneous information, while lightweight modal adaptors focus on modality unique features. Furthermore, we design an adaptive rank allocation strategy to adapt to the varying heterogeneity at different feature levels. When applied to two multimodal object detection datasets, experiments validate the effectiveness of our method. Notably, on DroneVehicle, LMA attains a 10.4% accuracy improvement over the state-of-the-art method with a 149M-parameters reduction. The code is available at https://github.com/zyszxhy/FoRA. Our work was submitted to ACM MM in April 2024, but was rejected. We will continue to refine our work and paper writing next, mainly including proof of theory and multi-task applications of FoRA.

cs.CV↗

arXiv2024

Leveraging Computer Vision in the Intensive Care Unit (ICU) for Examining Visitation and Mobility

Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations during rest hours can potentially exacerbate the risk of circadian rhythm disruption and delirium, it is not captured in the ICU. Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all. In the past few years, the computer vision field has found application in many domains by reducing the human burden. Using computer vision systems in the ICU can also potentially enable non-existing assessments or enhance the frequency and accuracy of existing assessments while reducing the staff workload. In this study, we leverage a state-of-the-art noninvasive computer vision system based on depth imaging to characterize ICU visitations and patients' mobility. We then examine the relationship between visitation and several patient outcomes, such as pain, acuity, and delirium. We found an association between deteriorating patient acuity and the incidence of delirium with increased visitations. In contrast, self-reported pain, reported using the Defense and Veteran Pain Rating Scale (DVPRS), was correlated with decreased visitations. Our findings highlight the feasibility and potential of using noninvasive autonomous systems to monitor ICU patients.

cs.CV↗

arXiv2024

Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Traditional CDSR models capture collaborative information through user and item modeling while overlooking valuable semantic information. Recently, Large Language Model (LLM) has demonstrated powerful semantic reasoning capabilities, motivating us to introduce them to better capture semantic information. However, introducing LLMs to CDSR is non-trivial due to two crucial issues: seamless information integration and domain-specific generation. To this end, we propose a novel framework named URLLM, which aims to improve the CDSR performance by exploring the User Retrieval approach and domain grounding on LLM simultaneously. Specifically, we first present a novel dual-graph sequential model to capture the diverse information, along with an alignment and contrastive learning method to facilitate domain knowledge transfer. Subsequently, a user retrieve-generation model is adopted to seamlessly integrate the structural information into LLM, fully harnessing its emergent inferencing ability. Furthermore, we propose a domain-specific strategy and a refinement module to prevent out-of-domain generation. Extensive experiments on Amazon demonstrated the information integration and domain-specific generation ability of URLLM in comparison to state-of-the-art baselines. Our code is available at https://github.com/TingJShen/URLLM

cs.LG↗

arXiv2024

Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior

The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e.g., $\ell_{2,1}$-norm). However, this may not be ideal since they overlook the spatial structure present in anomalies and make the detection result largely dependent on manually set sparsity. To tackle these problems, we redefine the optimization criterion for the anomaly component in the LRR model with a self-supervised network called self-supervised anomaly prior (SAP). This prior is obtained by the pretext task of self-supervised learning, which is customized to learn the characteristics of hyperspectral anomalies. Specifically, this pretext task is a classification task to distinguish the original hyperspectral image (HSI) and the pseudo-anomaly HSI, where the pseudo-anomaly is generated from the original HSI and designed as a prism with arbitrary polygon bases and arbitrary spectral bands. In addition, a dual-purified strategy is proposed to provide a more refined background representation with an enriched background dictionary, facilitating the separation of anomalies from complex backgrounds. Extensive experiments on various hyperspectral datasets demonstrate that the proposed SAP offers a more accurate and interpretable solution than other advanced HAD methods.

cs.CV↗

arXiv2024

A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation

Cross-domain recommendation (CDR), aiming to extract and transfer knowledge across domains, has attracted wide attention for its efficacy in addressing data sparsity and cold-start problems. Despite significant advances in representation disentanglement to capture diverse user preferences, existing methods usually neglect representation enhancement and lack rigorous decoupling constraints, thereby limiting the transfer of relevant information. To this end, we propose a Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation (AREIL). Specifically, we first divide user embeddings into domain-shared and domain-specific components to disentangle mixed user preferences. Then, we incorporate intra-domain and inter-domain information to adaptively enhance the ability of user representations. In particular, we propose a graph convolution module to capture high-order information, and a self-attention module to reveal inter-domain correlations and accomplish adaptive fusion. Next, we adopt domain classifiers and gradient reversal layers to achieve inversed representation learning in a unified framework. Finally, we employ a cross-entropy loss for measuring recommendation performance and jointly optimize the entire framework via multi-task learning. Extensive experiments on multiple datasets validate the substantial improvement in the recommendation performance of AREIL. Moreover, ablation studies and representation visualizations further illustrate the effectiveness of adaptive enhancement and inversed learning in CDR.

cs.IR↗

arXiv2024

APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model

The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable. Early detection of deteriorating conditions can result in providing timely interventions and improved survival rates. In this study, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a 150k-parameter state space-based neural network to predict acuity state, transitions, and the need for life-sustaining therapies in real-time in ICU patients. The model uses data obtained in the prior four hours in the ICU and patient information obtained at admission to predict the acuity outcomes in the next four hours. We validated APRICOT-M externally on data from hospitals not used in development (75,668 patients from 147 hospitals), temporally on data from a period not used in development (12,927 patients from one hospital from 2018-2019), and prospectively on data collected in real-time (215 patients from one hospital from 2021-2023) using three large datasets: the University of Florida Health (UFH) dataset, the electronic ICU Collaborative Research Database (eICU), and the Medical Information Mart for Intensive Care (MIMIC)-IV. The area under the receiver operating characteristic curve (AUROC) of APRICOT-M for mortality (external 0.94-0.95, temporal 0.97-0.98, prospective 0.96-1.00) and acuity (external 0.95-0.95, temporal 0.97-0.97, prospective 0.96-0.96) shows comparable results to state-of-the-art models. Furthermore, APRICOT-M can predict transitions to instability (external 0.81-0.82, temporal 0.77-0.78, prospective 0.68-0.75) and need for life-sustaining therapies, including mechanical ventilation (external 0.82-0.83, temporal 0.87-0.88, prospective 0.67-0.76), and vasopressors (external 0.81-0.82, temporal 0.73-0.75, prospective 0.66-0.74). This tool allows for real-time acuity monitoring in critically ill patients and can help clinicians make timely interventions.

cs.AI↗

arXiv2024

Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification

In multimodal land cover classification (MLCC), a common challenge is the redundancy in data distribution, where irrelevant information from multiple modalities can hinder the effective integration of their unique features. To tackle this, we introduce the Multimodal Informative Vit (MIVit), a system with an innovative information aggregate-distributing mechanism. This approach redefines redundancy levels and integrates performance-aware elements into the fused representation, facilitating the learning of semantics in both forward and backward directions. MIVit stands out by significantly reducing redundancy in the empirical distribution of each modality's separate and fused features. It employs oriented attention fusion (OAF) for extracting shallow local features across modalities in horizontal and vertical dimensions, and a Transformer feature extractor for extracting deep global features through long-range attention. We also propose an information aggregation constraint (IAC) based on mutual information, designed to remove redundant information and preserve complementary information within embedded features. Additionally, the information distribution flow (IDF) in MIVit enhances performance-awareness by distributing global classification information across different modalities' feature maps. This architecture also addresses missing modality challenges with lightweight independent modality classifiers, reducing the computational load typically associated with Transformers. Our results show that MIVit's bidirectional aggregate-distributing mechanism between modalities is highly effective, achieving an average overall accuracy of 95.56% across three multimodal datasets. This performance surpasses current state-of-the-art methods in MLCC. The code for MIVit is accessible at https://github.com/icey-zhang/MIViT.

cs.CV↗

arXiv2024

SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image

With recent advancements in aerospace technology, the volume of unlabeled remote sensing image (RSI) data has increased dramatically. Effectively leveraging this data through self-supervised learning (SSL) is vital in the field of remote sensing. However, current methodologies, particularly contrastive learning (CL), a leading SSL method, encounter specific challenges in this domain. Firstly, CL often mistakenly identifies geographically adjacent samples with similar semantic content as negative pairs, leading to confusion during model training. Secondly, as an instance-level discriminative task, it tends to neglect the essential fine-grained features and complex details inherent in unstructured RSIs. To overcome these obstacles, we introduce SwiMDiff, a novel self-supervised pre-training framework designed for RSIs. SwiMDiff employs a scene-wide matching approach that effectively recalibrates labels to recognize data from the same scene as false negatives. This adjustment makes CL more applicable to the nuances of remote sensing. Additionally, SwiMDiff seamlessly integrates CL with a diffusion model. Through the implementation of pixel-level diffusion constraints, we enhance the encoder's ability to capture both the global semantic information and the fine-grained features of the images more comprehensively. Our proposed framework significantly enriches the information available for downstream tasks in remote sensing. Demonstrating exceptional performance in change detection and land-cover classification tasks, SwiMDiff proves its substantial utility and value in the field of remote sensing.

cs.CV↗

arXiv2024

Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image

Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research. However, recent deep learning algorithms have predominantly focused on detecting cloud regions in satellite imagery, with insufficient attention to the specificity required for accurate cloud recognition. This limitation inspired us to develop the novel FY-4A-Himawari-8 (FYH) dataset, which includes nine distinct cloud categories and uses precise domain adaptation methods to align 70,419 image-label pairs in terms of projection, temporal resolution, and spatial resolution, thereby facilitating the training of supervised deep learning networks. Given the complexity and diversity of cloud formations, we have thoroughly analyzed the challenges inherent to cloud recognition tasks, examining the intricate characteristics and distribution of the data. To effectively address these challenges, we designed a Distribution-aware Interactive-Attention Network (DIAnet), which preserves pixel-level details through a high-resolution branch and a parallel multi-resolution cross-branch. We also integrated a distribution-aware loss (DAL) to mitigate the imbalance across cloud categories. An Interactive Attention Module (IAM) further enhances the robustness of feature extraction combined with spatial and channel information. Empirical evaluations on the FYH dataset demonstrate that our method outperforms other cloud recognition networks, achieving superior performance in terms of mean Intersection over Union (mIoU). The code for implementing DIAnet is available at https://github.com/icey-zhang/DIAnet.

cs.CV↗

arXiv2023

Expanding the Pressure Frontier in Grüneisen Parameter Measurement: Study of Sodium Chloride

The Grüneisen parameter (γ) is crucial for determining many thermal properties, including the anharmonic effect, thermostatistics, and equation of state (EOS) of materials. However, the isentropic adiabatic compression conditions required to measure the Grüneisen parameter under high pressure are difficult to achieve. Thus, direct experimental Grüneisen parameter data in a wide range of pressures is sparse. In this work, we developed a new device that can apply pressure (up to tens of GPa) with an extremely short time about 0.5 ms, confidently achieving isentropic adiabatic compression. Then, we applied our new technique to sodium chloride and measured its Grüneisen parameter, which conforms to previous theoretical predictions. According to our obtained sodium chloride Grüneisen parameters, the calculated Hugoniot curve of the NaCl B1 phase appears up to 20 GPa and 960 K, which compares very well with the shock compression experiment data by Fritz et al. and other calculation works. Our results suggest that this new method can reliably measure the Grüneisen parameter of even more materials, which is significant for researching the equation of state in substances.

cond-mat.mtrl-sci↗

arXiv2023

MDFL: Multi-domain Diffusion-driven Feature Learning

High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields. The spatial information in these images reflects the object's texture features, while the spectral information reveals the potential spectral representations across different bands. Currently, the understanding of high-dimensional images remains limited to a single-domain perspective with performance degradation. Motivated by the masking texture effect observed in the human visual system, we present a multi-domain diffusion-driven feature learning network (MDFL) , a scheme to redefine the effective information domain that the model really focuses on. This method employs diffusion-based posterior sampling to explicitly consider joint information interactions between the high-dimensional manifold structures in the spectral, spatial, and frequency domains, thereby eliminating the influence of masking texture effects in visual models. Additionally, we introduce a feature reuse mechanism to gather deep and raw features of high-dimensional data. We demonstrate that MDFL significantly improves the feature extraction performance of high-dimensional data, thereby providing a powerful aid for revealing the intrinsic patterns and structures of such data. The experimental results on three multi-modal remote sensing datasets show that MDFL reaches an average overall accuracy of 98.25%, outperforming various state-of-the-art baseline schemes. The code will be released, contributing to the computer vision community.

cs.CV↗

arXiv2023

The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)

Acuity assessments are vital in critical care settings to provide timely interventions and fair resource allocation. Traditional acuity scores rely on manual assessments and documentation of physiological states, which can be time-consuming, intermittent, and difficult to use for healthcare providers. Furthermore, such scores do not incorporate granular information such as patients' mobility level, which can indicate recovery or deterioration in the ICU. We hypothesized that existing acuity scores could be potentially improved by employing Artificial Intelligence (AI) techniques in conjunction with Electronic Health Records (EHR) and wearable sensor data. In this study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for developing an AI-driven acuity assessment score. Accelerometry data were collected from 86 patients wearing accelerometers on their wrists in an academic hospital setting. The data was analyzed using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (SOFA= Sequential Organ Failure Assessment) used as a baseline, particularly regarding the precision, sensitivity, and F1 score. The results showed that while a model relying solely on accelerometer data achieved limited performance (AUC 0.50, Precision 0.61, and F1-score 0.68), including demographic information with the accelerometer data led to a notable enhancement in performance (AUC 0.69, Precision 0.75, and F1-score 0.67). This work shows that the combination of mobility and patient information can successfully differentiate between stable and unstable states in critically ill patients.

cs.LG↗

arXiv2023

Transformers in Healthcare: A Survey

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of data, including medical imaging, structured and unstructured Electronic Health Records (EHR), social media, physiological signals, and biomolecular sequences. Those models could help in clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. We identified relevant studies using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.

cs.AI↗

arXiv2023

SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery

Accurately and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remains challenging. Most of the existing solutions primarily design complex deep neural networks to learn strong feature representations for objects separated from the background, which often results in a heavy computation burden. In this article, we propose an accurate yet fast object detection method for RSI, named SuperYOLO, which fuses multimodal data and performs high-resolution (HR) object detection on multiscale objects by utilizing the assisted super resolution (SR) learning and considering both the detection accuracy and computation cost. First, we utilize a symmetric compact multimodal fusion (MF) to extract supplementary information from various data for improving small object detection in RSI. Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy. Moreover, to avoid introducing additional computation, the SR branch is discarded in the inference stage, and the computation of the network model is reduced due to the LR input. Experimental results show that, on the widely used VEDAI RS dataset, SuperYOLO achieves an accuracy of 75.09% (in terms of mAP50 ), which is more than 10% higher than the SOTA large models, such as YOLOv5l, YOLOv5x, and RS designed YOLOrs. Meanwhile, the parameter size and GFLOPs of SuperYOLO are about 18 times and 3.8 times less than YOLOv5x. Our proposed model shows a favorable accuracy and speed tradeoff compared to the state-of-the-art models. The code will be open-sourced at https://github.com/icey-zhang/SuperYOLO.

cs.CV↗

arXiv2023

Predicting risk of delirium from ambient noise and light information in the ICU

Existing Intensive Care Unit (ICU) delirium prediction models do not consider environmental factors despite strong evidence of their influence on delirium. This study reports the first deep-learning based delirium prediction model for ICU patients using only ambient noise and light information. Ambient light and noise intensities were measured from ICU rooms of 102 patients from May 2021 to September 2022 using Thunderboard, ActiGraph sensors and an iPod with AudioTools application. These measurements were divided into daytime (0700 to 1859) and nighttime (1900 to 0659). Deep learning models were trained using this data to predict the incidence of delirium during ICU stay or within 4 days of discharge. Finally, outcome scores were analyzed to evaluate the importance and directionality of every feature. Daytime noise levels were significantly higher than nighttime noise levels. When using only noise features or a combination of noise and light features 1-D convolutional neural networks (CNN) achieved the strongest performance: AUC=0.77, 0.74; Sensitivity=0.60, 0.56; Specificity=0.74, 0.74; Precision=0.46, 0.40 respectively. Using only light features, Long Short-Term Memory (LSTM) networks performed best: AUC=0.80, Sensitivity=0.60, Specificity=0.77, Precision=0.37. Maximum nighttime and minimum daytime noise levels were the strongest positive and negative predictors of delirium respectively. Nighttime light level was a stronger predictor of delirium than daytime light level. Total influence of light features outweighed that of noise features on the second and fourth day of ICU stay. This study shows that ambient light and noise intensities are strong predictors of long-term delirium incidence in the ICU. It reveals that daytime and nighttime environmental factors might influence delirium differently and that the importance of light and noise levels vary over the course of an ICU stay.

cs.LG↗