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Yong Cao

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6 papers

2 author rows

AIIM Journal 2025 Journal Article

A cell-interacting and multi-correcting method for automatic circulating tumor cells detection

Xuan Zhang
Rensheng Lai
Ling Bai
Jianxin Ji
Ruihao Qin
Lihong Jiang
Bin Meng
Ying Zhang

Sensitive detection of circulating tumor cells (CTCs) from peripheral blood can serve as an effective tool in the early diagnosis and prognosis of cancer. Many methods based on modern object detectors were proposed in recent years for automatic abnormal cells detection in slide images. Although the modes of these methods can also be applied to the CTCs detection, several practical difficulties lead to suboptimal performance of them, such as accurate capture of CTCs in a large number of mixed cells and identification of CTCs and CTC-like cells with similar visual characteristics. Here, we develop a new cell-interacting and multi-correcting detector called CMD, and apply H&E-stained slide images to detect CTCs automatically for the first time. Specifically, the proposed method incorporates two task-oriented novel modules: (1) a self-attention module for aggregating feature interactions between cells and allowing the model to pay more attention to key abnormal cells, (2) a hard sample mining sampler for progressively correcting predictions of cells with ambiguous classification boundaries. Experiments conducted on a multi-center dataset of 1247 annotated slide images confirm the superiority of our method over state-of-the-art cell detection methods. The results of ablation experiment part also prove the effectiveness of two modules. The source codes of this paper are available at https: //github. com/zx333445/CMD.

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IROS Conference 2025 Conference Paper

Application of soft constraints on mirror position to improve robustness of optical target positioning in shallow water

Xiangjie Zhang
Taogang Hou
Hongde Qin
Haojie Li
Xiangxing Wang
Hao Wang
Yong Cao

The unique optical characteristics of the underwater environment, such as light refraction and loss of salient features, pose a significant challenge to traditional vision sensors, especially in the swarm operation scenario where multiple autonomous underwater vehicles (AUVs) cooperate with the mother ship for positioning. To address these challenges, this study proposes the application of soft constraints on mirror position to improve the robustness of optical target positioning in shallow water. During the mapping phase, we establish and optimize the pose relationships between ArUco markers and their mirrors, thereby expanding the locatable space for AUVs (Autonomous Underwater Vehicles). With the arrangement of ArUco markers unchanged, the number of usable markers doubles. Surface mirror-assisted positioning provides more visual features and additional computed corner points, enhancing the reliability of camera observations and improving positioning accuracy. Experimental results demonstrate that, compared to classical algorithms from the Artificial Vision Applications (A. V. A) laboratory, our method improves position accuracy by 25. 8% in single-marker scenarios and by 14. 7% in multi-marker scenarios. Therefore, our method provides enhanced mapping and positioning for AUVs in shallow water areas where optical markers can be deployed. This study provides a new mapping paradigm and multi-body localization solution for optical marker-guided underwater swarm operations.

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YNIMG Journal 2025 Journal Article

Sleep indicators and staging: A functional near-infrared spectroscopy study in healthy young adults

Yong Cao
Xingwei An
Wenxiao Zhong
Jin Jiang
Hongzuo Chu
Xuejun Jiao
Xiaoping Chen
Yufeng Ke

Functional near-infrared spectroscopy(fNIRS)-based sleep staging has attracted considerable interest due to its portability and limited interference with sleep. However, few studies have systematically examined sleep indicators or formulated sleep staging models based on fNIRS features labelled by polysomnography(PSG). This study aimed to address these shortcomings and promote the application of fNIRS in sleep monitoring. 37 volunteers participated in our experiment, with 6-channel prefrontal fNIRS data and standard PSG data collected simultaneously. Sleep indicators were extracted from time-domain, frequency-domain, and entropy perspectives. Sleep staging was developed based on these indicators using human-scored PSG as reference. Our findings indicated deeper sleep was correlated with a decrease in amplitude of time-domain features, while entropy features showed a contrasting trend. The fNIRS-based sleep staging achieved a Cohen's kappa(κ) of 0.76±0.12, 0.72±0.09, 0.71±0.07, with accuracies of 94.2 ± 2.4 %, 87.8 ± 3.2 %, and 82.2 ± 4.1 %, for 2-class(Wake/Sleep), 3-class(Wake/NREM/REM), 4-class (Wake/N1+N2/N3/REM) classifications, respectively. Sleep statistics derived from fNIRS closely aligned with those from PSG, with differences in sleep onset latency, wake after sleep onset, total wake/sleep time within 5 min and sleep efficiency below 3 %. The substantial agreement in both detailed (epoch-by-epoch) and comprehensive (total) sleep statistics with PSG suggests fNIRS is a reliable tool for long-term sleep monitoring in everyday settings.

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IJCAI Conference 2024 Conference Paper

Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs

Zhengdao Li
Yong Cao
Kefan Shuai
Yiming Miao
Kai Hwang

Graph classification benchmarks, vital for assessing and developing graph neural network (GNN) models, have recently been scrutinized, as simple methods like MLPs have demonstrated comparable performance. This leads to an important question: Do these benchmarks effectively distinguish the advancements of GNNs over other methodologies? If so, how do we quantitatively measure this effectiveness? In response, we first propose an empirical protocol based on a fair benchmarking framework to investigate the performance discrepancy between simple methods and GNNs. We further propose a novel metric to quantify the dataset effectiveness by considering both dataset complexity and model performance. To the best of our knowledge, our work is the first to thoroughly study and provide an explicit definition for dataset effectiveness in the graph learning area. Through testing across 16 real-world datasets, we found our metric to align with existing studies and intuitive assumptions. Finally, we explore the causes behind the low effectiveness of certain datasets by investigating the correlation between intrinsic graph properties and class labels, and we developed a novel technique supporting the correlation-controllable synthetic dataset generation. Our findings shed light on the current understanding of benchmark datasets, and our new platform could fuel the future evolution of graph classification benchmarks.

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AAAI Conference 2018 Conference Paper

CoLink: An Unsupervised Framework for User Identity Linkage

Zexuan Zhong
Yong Cao
Mu Guo
Zaiqing Nie

Nowadays, it is very common for one person to be in different social networks. Linking identical users across different social networks, also known as the User Identity Linkage (UIL) problem, is fundamental for many applications. There are two major challenges in the UIL problem. First, it’s extremely expensive to collect manually linked user pairs as training data. Second, the user attributes in different networks are usually deﬁned and formatted very differently which makes attribute alignment very hard. In this paper we propose CoLink, a general unsupervised framework for the UIL problem. CoLink employs a co-training algorithm, which manipulates two independent models, the attribute-based model and the relationship-based model, and makes them reinforce each other iteratively in an unsupervised way. We also propose the sequence-to-sequence learning as a very effective implementation of the attribute-based model, which can well handle the challenge of the attribute alignment by treating it as a machine translation problem. We apply CoLink to a UIL task of mapping the employees in an enterprise network to their LinkedIn proﬁles. The experiment results show that CoLink generally outperforms the state-of-the-art unsupervised approaches by an F1 increase over 20%.

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AAAI Conference 2014 Conference Paper

Learning Word Representation Considering Proximity and Ambiguity

Lin Qiu
Yong Cao
Zaiqing Nie
Yong Yu
Yong Rui

Distributed representations of words (aka word embedding) have proven helpful in solving natural language processing (NLP) tasks. Training distributed representations of words with neural networks has lately been a major focus of researchers in the field. Recent work on word embedding, the Continuous Bag-of-Words (CBOW) model and the Continuous Skip-gram (Skip-gram) model, have produced particularly impressive results, significantly speeding up the training process to enable word representation learning from largescale data. However, both CBOW and Skip-gram do not pay enough attention to word proximity in terms of model or word ambiguity in terms of linguistics. In this paper, we propose Proximity-Ambiguity Sensitive (PAS) models (i. e. PAS CBOW and PAS Skip-gram) to produce high quality distributed representations of words considering both word proximity and ambiguity. From the model perspective, we introduce proximity weights as parameters to be learned in PAS CBOW and used in PAS Skip-gram. By better modeling word proximity, we reveal the strength of pooling-structured neural networks in word representation learning. The proximitysensitive pooling layer can also be applied to other neural network applications that employ pooling layers. From the linguistics perspective, we train multiple representation vectors per word. Each representation vector corresponds to a particular group of POS tags of the word. By using PAS models, we achieved a 16. 9% increase in accuracy over state-of-theart models.

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