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Hao Wen

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6 papers

2 author rows

EAAI Journal 2025 Journal Article

Enhanced pediatric pneumonia auxiliary diagnosis: Integrating optical fiber vibration sensing with machine learning

Pengfei Cao
Jiawei Xu
Yifan Zhao
Qian Ni
Yuxia Li
Hansen Chen
Ming Song
Jiqiang Shang

Childhood pneumonia remains a primary cause of death in children under five. Early detection is arduous due to its inconspicuous symptoms, and the existing radiological diagnostic techniques carry the risk of radiation - induced harm to young patients. To overcome these limitations, a groundbreaking study has proposed an innovative non - invasive diagnostic approach that integrates fiber optic vibration sensing technology with machine learning algorithms for pediatric pneumonia diagnosis. A novel fiber optic sensor is engineered to precisely capture respiratory vibration signals (RVS). These signals are then processed and analyzed using a Stacked - Grid Search Ensemble Learning Model (SGELM). In the experiment, respiratory vibration signals were gathered from 1649 pediatric patients aged between 3 and 14 who suffered from respiratory diseases. Through data balancing techniques, the dataset was expanded to 2184 samples. This dataset was partitioned into training, testing, and validation subsets. The developed system exhibited remarkable performance on the test dataset. It achieved high levels of accuracy, sensitivity, and specificity. Notably, it was also capable of classifying different pneumonia pathological types and statuses. This innovative method not only mitigates the radiation - related risks associated with traditional diagnostic methods but also holds great promise in revolutionizing the diagnosis of pediatric respiratory diseases. It could potentially improve the early - diagnosis rate and contribute to better treatment outcomes for children with pneumonia, thus playing a significant role in enhancing child health globally.

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YNIMG Journal 2025 Journal Article

NaDyNet: A toolbox for dynamic network analysis of naturalistic stimuli

Junjie Yang
Zhe Hu
Junjing Li
Xiaolin Guo
Xiaowei Gao
Jiaxuan Liu
Yaling Wang
Zhiheng Qu

Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of naturalistic stimuli is rich and continuous. The fMRI signals of naturalistic stimuli are complex and include different components. A major challenge is isolate the stimuli-induced signals while simultaneously tracking the brain's responses to these stimuli in real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic brain network analysis methods and their improved versions. The main features of NaDyNet are: 1) extracting signals of interest from naturalistic fMRI signals; 2) incorporating six commonly used dynamic analysis methods and three static analysis methods; 3) improved versions of these dynamic methods by adopting inter-subject analysis to eliminate the effects of non-interest signals; 4) performing K-means clustering analysis to identify temporally reoccurring states along with their temporal and spatial attributes; 5) Visualization of spatiotemporal results. We then introduced the rationale for incorporating inter-subject analysis to improve existing dynamic brain network analysis methods and presented examples by analyzing naturalistic fMRI data. We hope that this toolbox will promote the development of naturalistic neuroscience. The toolbox is available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

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NeurIPS Conference 2024 Conference Paper

WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark

Chunhui Zhang
Li Liu
Guanjie Huang
Hao Wen
Xi Zhou
Yanfeng Wang

Underwater Object Tracking (UOT) is essential for identifying and tracking submerged objects in underwater videos, but existing datasets are limited in scale, diversity of target categories and scenarios covered, impeding the development of advanced tracking algorithms. To bridge this gap, we take the first step and introduce WebUOT-1M, \ie, the largest public UOT benchmark to date, sourced from complex and realistic underwater environments. It comprises 1. 1 million frames across 1, 500 video clips filtered from 408 target categories, largely surpassing previous UOT datasets, \eg, UVOT400. Through meticulous manual annotation and verification, we provide high-quality bounding boxes for underwater targets. Additionally, WebUOT-1M includes language prompts for video sequences, expanding its application areas, \eg, underwater vision-language tracking. Given that most existing trackers are designed for open-air conditions and perform poorly in underwater environments due to domain gaps, we propose a novel framework that uses omni-knowledge distillation to train a student Transformer model effectively. To the best of our knowledge, this framework is the first to effectively transfer open-air domain knowledge to the UOT model through knowledge distillation, as demonstrated by results on both existing UOT datasets and the newly proposed WebUOT-1M. We have thoroughly tested WebUOT-1M with 30 deep trackers, showcasing its potential as a benchmark for future UOT research. The complete dataset, along with codes and tracking results, are publicly accessible at \href{https: //github. com/983632847/Awesome-Multimodal-Object-Tracking}{\color{magenta}{here}}.

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ICRA Conference 2021 Conference Paper

End-to-End Semi-supervised Learning for Differentiable Particle Filters

Hao Wen
Xiongjie Chen
Georgios Papagiannis
Conghui Hu
Yunpeng Li 0001

Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications. The dynamic and measurement models in this framework are learnable through the differentiable implementation of particle filters. Past efforts in optimising such models often require the knowledge of true states which can be expensive to obtain or even unavailable in practice. In this paper, in order to reduce the demand for annotated data, we present an end-to-end learning objective based upon the maximisation of a pseudo-likelihood function which can improve the estimation of states when large portion of true states are unknown. We assess performance of the proposed method in state estimation tasks in robotics with simulated and real-world datasets.

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AAAI Conference 2020 Conference Paper

Coupled-View Deep Classifier Learning from Multiple Noisy Annotators

Shikun Li
Shiming Ge
Yingying Hua
Chunhui Zhang
Hao Wen
Tengfei Liu
Weiqiang Wang

Typically, learning a deep classiﬁer from massive cleanly annotated instances is effective but impractical in many realworld scenarios. An alternative is collecting and aggregating multiple noisy annotations for each instance to train the classiﬁer. Inspired by that, this paper proposes to learn deep classiﬁer from multiple noisy annotators via a coupled-view learning approach, where the learning view from data is represented by deep neural networks for data classiﬁcation and the learning view from labels is described by a Naive Bayes classiﬁer for label aggregation. Such coupled-view learning is converted to a supervised learning problem under the mutual supervision of the aggregated and predicted labels, and can be solved via alternate optimization to update labels and reﬁne the classiﬁers. To alleviate the propagation of incorrect labels, small-loss metric is proposed to select reliable instances in both views. A co-teaching strategy with class-weighted loss is further leveraged in the deep classiﬁer learning, which uses two networks with different learning abilities to teach each other, and the diverse errors introduced by noisy labels can be ﬁltered out by peer networks. By these strategies, our approach can ﬁnally learn a robust data classiﬁer which less overﬁts to label noise. Experimental results on synthetic and real data demonstrate the effectiveness and robustness of the proposed approach.

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AAAI Conference 2020 Conference Paper

Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition

Shiming Ge
Kangkai Zhang
Haolin Liu
Yingying Hua
Shengwei Zhao
Xin Jin
Hao Wen

In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due to the missing of informative details during resolution degradation. However, these images are still recognizable for subjects who are familiar with the corresponding high-resolution ones. Inspired by that, we propose a teacherstudent learning approach to facilitate low-resolution image recognition via hybrid order relational knowledge distillation. The approach refers to three streams: the teacher stream is pretrained to recognize high-resolution images in high accuracy, the student stream is learned to identify low-resolution images by mimicking the teacher’s behaviors, and the extra assistant stream is introduced as bridge to help knowledge transfer across the teacher to the student. To extract sufﬁcient knowledge for reducing the loss in accuracy, the learning of student is supervised with multiple losses, which preserves the similarities in various order relational structures. In this way, the capability of recovering missing details of familiar low-resolution images can be effectively enhanced, leading to a better knowledge transfer. Extensive experiments on metric learning, low-resolution image classiﬁcation and lowresolution face recognition tasks show the effectiveness of our approach, while taking reduced models.

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