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Yufei Jin

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5 papers

2 author rows

ICRA Conference 2025 Conference Paper

Continuous Convolution for Automated Measurement of Sperm Flagella

Yufei Jin
Han Yang
Wenyuan Chen
Xinrui Wang
Yu Sun 0001
Zhuoran Zhang 0001

Quantifying sperm flagellar beating behavior (e. g. , beating amplitude, frequency, and wavelength) plays a crucial role in biological research, clinical diagnostics, and the design of sperm-inspired microrobots. However, existing computational methods struggle to accurately and efficiently analyze the highly dynamic, complex, and fine structures of sperm flagella, especially when portions of the flagellum become invisible due to three-dimensional out-of-focus beating. This paper proposes an automated high-throughput tool for quantitative analysis of sperm flagellar beating. The core innovation is continuous convolution (CConv), which adaptively captures the irregular, time-varying patterns of sperm flagella while ensuring continuity in segmentation outputs, even in the presence of locally invisible regions caused by out-of-focus motion. CConv can be integrated into various neural network architectures as a plug-and-play module. Extensive experiments demonstrate that integrating CConv consistently improves the accuracy and continuity of flagella segmentation across different networks. Furthermore, utilizing a curvature-based approach, we quantified key flagellar beating parameters, including length, amplitude, frequency, and wavelength. Applying the high-throughput tool on 1200 sperm revealed that sperm from fertile donors had significantly higher flagellar beating frequency than sperm from infertile patients. The proposed automated tool unlocks high-throughput, quantitative analysis of sperm flagellar beating, showing the potential for applications in reproductive biology and engineering research. The codes and datasets will be released at https://github.com/Goldfish-Yu/CConv.

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IJCAI Conference 2025 Conference Paper

HGEN: Heterogeneous Graph Ensemble Networks

Jiajun Shen
Yufei Jin
Kaibu Feng
Yi He
Xingquan Zhu

This paper presents HGEN that pioneers ensemble learning for heterogeneous graphs. We argue that the heterogeneity in node types, nodal features, and local neighborhood topology poses significant challenges for ensemble learning, particularly in accommodating diverse graph learners. Our HGEN framework ensembles multiple learners through a meta-path and transformation-based optimization pipeline to uplift classification accuracy. Specifically, HGEN uses meta-path combined with random dropping to create Allele Graph Neural Networks (GNNs), whereby the base graph learners are trained and aligned for later ensembling. To ensure effective ensemble learning, HGEN presents two key components: 1) a residual-attention mechanism to calibrate allele GNNs of different meta-paths, thereby enforcing node embeddings to focus on more informative graphs to improve base learner accuracy, and 2) a correlation-regularization term to enlarge the disparity among embedding matrices generated from different meta-paths, thereby enriching base learner diversity. We analyze the convergence of HGEN and attest its higher regularization magnitude over simple voting. Experiments on five heterogeneous networks validate that HGEN consistently outperforms its state-of-the-art competitors by substantial margin. Codes are available at https: //github. com/Chrisshen12/HGEN.

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AAAI Conference 2024 Conference Paper

GLDL: Graph Label Distribution Learning

Yufei Jin
Richard Gao
Yi He
Xingquan Zhu

Label Distribution Learning (LDL), as a more general learning setting than generic single-label and multi-label learning, has been commonly used in computer vision and many other applications. To date, existing LDL approaches are designed and applied to data without considering the interdependence between instances. In this paper, we propose a Graph Label Distribution Learning (GLDL) framework, which explicitly models three types of relationships: instance-instance, label-label, and instance-label, to learn the label distribution for networked data. A label-label network is learned to capture label-to-label correlation, through which GLDL can accurately learn label distributions for nodes. Dual graph convolution network (GCN) Co-training with heterogeneous message passing ensures two GCNs, one focusing on instance-instance relationship and the other one targeting label-label correlation, are jointly trained such that instance-instance relationship can help induce label-label correlation and vice versa. Our theoretical study derives the error bound of GLDL. For verification, four benchmark datasets with label distributions for nodes are created using common graph benchmarks. The experiments show that considering dependency helps learn better label distributions for networked data, compared to state-of-the-art LDL baseline. In addition, GLDL not only outperforms simple GCN and graph attention networks (GAT) using distribution loss but is also superior to its variant considering label-label relationship as a static network. GLDL and its benchmarks are the first research endeavors to address LDL for graphs. Code and benchmark data are released for public access.

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NeurIPS Conference 2024 Conference Paper

HGDL: Heterogeneous Graph Label Distribution Learning

Yufei Jin
Heng Lian
Yi He
Xingquan Zhu

Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification. This paper advances LDL into graph domains and aims to tackle a novel and fundamentalheterogeneous graph label distribution learning (HGDL) problem. We argue that the graph heterogeneity reflected on node types, node attributes, and neighborhood structures can impose significant challenges for generalizing LDL onto graphs. To address the challenges, we propose a new learning framework with two key components: 1) proactive graph topology homogenization, and 2) topology and content consistency-aware graph transformer. Specifically, the former learns optimal information aggregation between meta-paths, so that the nodeheterogeneity can be proactively addressed prior to the succeeding embedding learning; the latter leverages an attention mechanism to learn consistency between meta-path and node attributes, allowing network topology and nodal attributes to be equally emphasized during the label distribution learning. By using KL-divergence and additional constraints, \method~delivers an end-to-end solution for learning and predicting label distribution for nodes. Both theoretical and empirical studies substantiate the effectiveness of our HGDL approach. Our code and datasets are available at https: //github. com/Listener-Watcher/HGDL.

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ICRA Conference 2024 Conference Paper

Weakly-Supervised Depth Completion during Robotic Micromanipulation from a Monocular Microscopic Image

Han Yang
Yufei Jin
Guanqiao Shan
Yibin Wang
Yongbin Zheng
Jiangfan Yu
Yu Sun 0001
Zhuoran Zhang 0001

Obtaining three-dimensional information, especially the z-axis depth information, is crucial for robotic micromanipulation. Due to the unavailability of depth sensors such as lidars in micromanipulation setups, traditional depth acquisition methods such as depth from focus or depth from defocus directly infer depth from microscopic images and suffer from poor resolution. Alternatively, micromanipulation tasks obtain accurate depth information by detecting the contact between an end-effector and an object (e. g. , a cell). Despite its high accuracy, only sparse depth data can be obtained due to its low efficiency. This paper aims to address the challenge of acquiring dense depth information during robotic cell micromanipulation. A weakly-supervised depth completion network is proposed to take cell images and sparse depth data obtained by contact detection as input to generate a dense depth map. A two-stage data augmentation method is proposed to augment the sparse depth data, and the depth map is optimized by a network refinement method. The experimental results show that the MAE value of the depth prediction error is less than 0. 3 µm, which proves the accuracy and effectiveness of the method. This deep learning network pipeline can be seamlessly integrated with the robotic micromanipulation tasks to provide accurate depth information.

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