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Yi-Ping Phoebe Chen

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10 papers
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10

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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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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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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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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