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Yi-Ping Phoebe Chen

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10 papers

1 author row

AAAI Conference 2025 Conference Paper

Temporal Coherent Object Flow for Multi-Object Tracking

Zikai Song
Run Luo
Lintao Ma
Ying Tang
Yi-Ping Phoebe Chen
Junqing Yu
Wei Yang

Multi-object tracking is a challenging vision task that requires simultaneous reasoning about object detection and object association. Conventional solutions use frame as the basic unit and typically rely on a motion predictor that exploits the appearance features to associate detected candidates, leading to insufficient adaptability to long-term associations. In this study, we propose a section-based multi-object tracking approach that integrates a temporal coherent Object Flow Tracker (OFTrack), capable of achieving simultaneous multi-frame tracking by treating multiple consecutive frames as the basic processing unit, denoted as a “section”. Our OFTrack boosts the optical flow to the object flow by employing object perception and section-based motion estimation strategies. Object perception adopts object-aware sampling and scale-aware correlation to enable precise target discrimination. Motion estimation models the correlation of different objects in multi-frames via specialized temporal-spatial attention to achieve robust association in very long videos. Additionally, to address the oscillation of unpredictable trajectories in multi-frame estimation, we have designed temporal coherent enhancement including the trajectory masking pre-training and the smoothing constraint on trajectory curves. Comprehensive experiments on several widely used benchmarks demonstrate the superior performance of our approach.

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JBHI Journal 2025 Journal Article

The Large Language Models on Biomedical Data Analysis: A Survey

Wei Lan
Zhentao Tang
Mingyang Liu
Qingfeng Chen
Wei Peng
Yi-Ping Phoebe Chen
Yi Pan

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research.

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EAAI Journal 2024 Journal Article

The integration of knowledge graph convolution network with denoising autoencoder

Gurinder Kaur
Fei Liu
Yi-Ping Phoebe Chen

The knowledge graph convolution network (KGCN) is a recommendation model that provides a set of top recommendations based on knowledge graph developed between users, items, and their attributes. In this study, we integrate the KGCN model with denoising autoencoder (DAE) to improve its recommendation performance. A trained DAE is used to sample K-dimensional latent representation for each user, which then transforms that representation to generate a probability distribution over items. The relationship between acquired latent representation and the meta features is modelled using multivariate multiple regression (MMR) kernel. As a result, without the need for new configuration assessments, performance estimation of new data is pursued directly through MMR and the decoder of DAE. Empirically, we demonstrate that on real-world datasets, the proposed method substantially outperforms other state-of-the-art baselines. Movie-Lens 100K (ML-100K) and Movie-Lens 1M (ML-1M), two common MovieLens datasets, are used to verify the accuracy of the proposed approach. The results from experiments show significant improvement of 41. 17% when the proposed method is applied on KGCN model. The proposed framework outperforms other state-of-the-art frameworks on Recall@K and normalized discounted cumulative gain (NDCG@K) metrics by achieving higher scores for Recall@5, Recall@10, NDCG@1, and NDCG@10.

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AAAI Conference 2023 Conference Paper

Compact Transformer Tracker with Correlative Masked Modeling

Zikai Song
Run Luo
Junqing Yu
Yi-Ping Phoebe Chen
Wei Yang

Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism. Most recent advances focus on exploring attention mechanism variants for better information aggregation. We find these schemes are equivalent to or even just a subset of the basic self-attention mechanism. In this paper, we prove that the vanilla self-attention structure is sufficient for information aggregation, and structural adaption is unnecessary. The key is not the attention structure, but how to extract the discriminative feature for tracking and enhance the communication between the target and search image. Based on this finding, we adopt the basic vision transformer (ViT) architecture as our main tracker and concatenate the template and search image for feature embedding. To guide the encoder to capture the invariant feature for tracking, we attach a lightweight correlative masked decoder which reconstructs the original template and search image from the corresponding masked tokens. The correlative masked decoder serves as a plugin for the compact transformer tracker and is skipped in inference. Our compact tracker uses the most simple structure which only consists of a ViT backbone and a box head, and can run at 40 fps. Extensive experiments show the proposed compact transform tracker outperforms existing approaches, including advanced attention variants, and demonstrates the sufficiency of self-attention in tracking tasks. Our method achieves state-of-the-art performance on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks. Our project is available at https://github.com/HUSTDML/CTTrack.

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AIIM Journal 2023 Journal Article

DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder

S. Qasim Abbas
Lianhua Chi
Yi-Ping Phoebe Chen

The growing prevalence of neurological disorders, e. g. , Autism Spectrum Disorder (ASD), demands robust computer-aided diagnosis (CAD) due to the diverse symptoms which require early intervention, particularly in young children. The absence of a benchmark neuroimaging diagnostics paves the way to study transitions in the brain's anatomical structure and neurological patterns associated with ASD. The existing CADs take advantage of the large-scale baseline dataset from the Autism Brain Imaging Data Exchange (ABIDE) repository to improve diagnostic performance, but the involvement of multisite data also amplifies the variabilities and heterogeneities that hinder satisfactory results. To resolve this problem, we propose a Deep Multimodal Neuroimaging Framework (DeepMNF) that employs Functional Magnetic Resonance Imaging (fMRI) and Structural Magnetic Resonance Imaging (sMRI) to integrate cross-modality spatiotemporal information by exploiting 2-dimensional time-series data along with 3-dimensional images. The purpose is to fuse complementary information that increases group differences and homogeneities. To the best of our knowledge, our DeepMNF achieves superior validation performance than the best reported result on the ABIDE-1 repository involving datasets from all available screening sites. In this work, we also demonstrate the performance of the studied modalities in a single model as well as their possible combinations to develop the multimodal framework.

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AIIM Journal 2021 Journal Article

Tracking leukocytes in intravital time lapse images using 3D cell association learning network

Marzieh R. Moghadam
Yi-Ping Phoebe Chen

Leukocytes are key cellular elements of the innate immune system in all vertebrates, which play a crucial role in defending organisms against invading pathogens. Tracking these highly migratory and amorphous cells in in vivo models such as zebrafish embryos is a challenging task in cellular immunology. As temporal and special analysis of these imaging datasets by a human operator is quite laborious, developing an automated cell tracking method is highly in demand. Despite the remarkable advances in cell detection, this field still lacks powerful algorithms to accurately associate the detected cell across time frames. The cell association challenge is mostly related to the amorphous nature of cells, and their complicated motion profile through their migratory paths. To tackle the cell association challenge, we proposed a novel deep-learning-based object linkage method. For this aim, we trained the 3D cell association learning network (3D-CALN) with enough manually labelled paired 3D images of single fluorescent zebrafish's neutrophils from two consecutive frames. Our experiment results prove that deep learning is significantly applicable in cell linkage and particularly for tracking highly mobile and amorphous leukocytes. A comparison of our tracking accuracy with other available tracking algorithms shows that our approach performs well in relation to addressing cell tracking problems.

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JBHI Journal 2021 Journal Article

Tracking Neutrophil Migration in Zebrafish Model Using Multi-Channel Feature Learning

Marzieh R. Moghadam
Yi-Ping Phoebe Chen

Tracking cells over time is crucial in the fields of computer vision and biomedical science. Studying neutrophils and their migratory profile is the highly topical fields in inflammation research due to determining role of these cells during immune responses. As neutrophils generally are of various shapes and motion, it remains challenging to track and describe their behaviours from multi-dimensional microscopy datasets. In this study, we propose a robust novel multi-channel feature learning (MCFL) model inspired by deep learning to extract the complex behaviour of neutrophils moved in time lapse images. In this model, the convolutional neural networks along with cell relocation distance and orientation channels learn the robust significant spatial and temporal features of an individual neutrophil. Additionally, we also proposed a new cell tracking framework to detect and track neutrophils in the original time-laps microscopy images, entails sampling, observation, and visualisation functions. Our proposed cell tracking-based-multi channel feature learning method has remarkable performance in rectifying common cell tracking problem compared with state-of the-art methods.

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AIJ Journal 2019 Journal Article

Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods

Hao-Fan Yang
Yi-Ping Phoebe Chen

Time series analysis has become more accurate with the emergence of powerful modelling methods based on machine learning development. Prediction models use historical time series to predict future conditions that occur over periods of time. However, most of these models are shallow models, only containing a small number of non-linear operations and without the ability or the capacity to extract underlying features from complex time series accurately. Moreover, deep learning approaches outperform statistical and computational approaches if a large amount of data and/or hidden layers are involved in the development of a forecasting model, but they are criticized for their relatively slow learning speeds. Therefore, this research proposes a hybrid model, which is hybridized by empirical mode decomposition, stacked auto-encoders, and extreme learning machines, aiming to forecast wind speed accurately and efficiently. The evaluation is undertaken by conducting extensive experiments using real-world data. The results show that the proposed E-S-ELM can accurately and efficiently forecast wind speed, and the effectiveness of the shared-hidden-layer approach for deep networks is also demonstrated.

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IS Journal 2016 Journal Article

Interval-Based Similarity for Classifying Conserved RNA Secondary Structures

Qingfeng Chen
Yi-Ping Phoebe Chen
Chengqi Zhang

The structure of a molecule is critical to determine its function in a biological context. Intelligent strategies are required to group structures with high similarity in an intuitive way. Most previous approaches focus on addressing sequence similarity and gene expression, whereas the techniques for comparative analysis of conserved structure data are underdeveloped because various secondary structures are complex, and most existing distance metrics have limitations. This article proposes a novel classification schema in terms of interval-based and weighted similarity functions that considers the intersection, non-intersection, and inclusion between two intervals of sequence size for conserved structures. The secondary structures are characterized by distance vectors. This assists in classifying structures under specific structure patterns that are expected to be correlated by functional or structural importance.

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IS Journal 2012 Journal Article

Discovering Inhibition Pathways for Protein Kinases

Qingfeng Chen
Yi-Ping Phoebe Chen

The inhibition of protein kinase activity can cause disease. The authors present a method for investigating inhibitive regulatory correlations between kinase isoforms and physical factors.

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