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Shuiping Gou

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8 papers

1 author row

EAAI Journal 2026 Journal Article

Mutual risk prompt learning with multi-objective optimization for collaborative tumor and peritumor segmentation

Nuo Tong
Qingyang Meng
Chunsheng Xu
Changhao Liu
Shuiping Gou
Mei Shi
Mengbin Li

Early radical surgery, radiotherapy, and other treatments may offer curative effects for tumors. However, the proximity of the tumor to surrounding organs-at-risk (OARs) significantly influences both the surgical outcome and prognosis. For benign tumors, the risk is primarily associated with the tumor's boundaries. In contrast, for malignant tumors, the main challenge lies in balancing the preservation of surrounding organ function while minimizing the risk of tumor recurrence. Therefore, understanding the tumor's characteristics and its anatomical relationships with OARs are essential. Most of the existing studies neglect the constrained interrelations and the potential optimization conflicts between tumor and OARs and easily introduce risks and uncertainties in tumor treatment and OARs protection. Here, we propose a novel multi-objective segmentation network for tumor and OARs, called ROJS-Net, which incorporates mutual risk prompt learning and multi-gate mixture of experts to achieve risk-optimized collaborative segmentation. A multi-task learning framework with shared encoder and multiple expert decoders are employed as the network backbone. Mutual risk prompt learning module is developed to obtain the target-specific features and perform mutual risk recalibration between features of different targets, enabling a comprehensive understanding of the anatomical environment. The risk-recalibrated features are then fed into the task-specific gating network to adaptively activate the highly-correlated expert decoders, generating the final segmentation results. Extensive experiments conducted on both benign and malignant tumor datasets demonstrate the effectiveness of the proposed ROJS-Net. These results validate that ROJS-Net effectively resolves the optimization divergence, facilitating risk-controllable treatment planning in various clinical settings.

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AAAI Conference 2026 Conference Paper

TIM++: Transductive Information Maximization for Few-Shot CLIP

Yingping Li
Yutong Zou
Yunshi Huang
Changzhe Jiao
Xinlin Wang
Shen Peng
Zhang Guo
Shuiping Gou

Transductive Information Maximization (TIM) is a leading transductive few-shot learning method that maximizes the mutual information between query features and their predicted labels, while incorporating supervision from the support set. However, its potential remains underexplored, primarily due to the limited utilization of textual knowledge provided by vision-language models (VLMs) such as CLIP. To address this, we propose TIM++, an enhanced framework that incorporates both visual and textual information for few-shot CLIP adaptation. Specifically, TIM++ introduces a Kullback-Leibler (KL) divergence-based regularization term that encourages the model’s posterior predictions to align with CLIP’s zero-shot output distribution, especially focusing on the most confident predictions. Additionally, we develop an improved prototype initialization strategy that leverages both support and query features enriched with CLIP-guided semantics. Extensive experiments on 11 public datasets demonstrate that TIM++ consistently outperforms the standard TIM, achieving average accuracy gains of 19.25% and 10.88% in 1-shot and 2-shot settings, respectively. TIM++ also surpasses other existing state-of-the-art methods, establishing a new benchmark for few-shot learning with VLMs.

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JBHI Journal 2025 Journal Article

Multi-Task Hybrid Conv-Transformer With Emotional Localized Ambiguity Exploration for Facial Pain Assessment

Shasha Mao
Angze Li
Yanjia Luo
Shuiping Gou
Mengnan Qi
Tianhuan Li
Xinyi Wei
Binxiao Su

Recently, there has been significant progress in automatic pain assessment based on facial expression analysis. However, the performance of pain assessment remains unsatisfactory, due to a lack of analysis on local pain-related action units and emotional ambiguity. In particular, ambiguous pain expressions complicate the estimation of pain. It is argued that certain facial local regions related to pain should receive more attention while estimating pain intensities. Based on this, we propose a multi-task hybrid Conv-Transformer method for facial pain assessment, which utilizes the self-attention mechanism to explore facial local features related to pain intensities and constructs a multi-task joint optimizing module to mitigate facial emotional ambiguity. In particular, the proposed method modifies the network structure of the vision transformer model to better estimate continuous pain intensities. Meanwhile, a multi-task module is constructed to jointly optimize the classification and the regression tasks of pain assessment, which effectively regularizes the extracted features and facilitates a better fit of the regressed prediction to the given label. Finally, experimental results on the UNBC Pain dataset illustrate that the proposed method performs better with pain assessment compared with state-of-the-art methods.

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JBHI Journal 2025 Journal Article

Self-Supervised, Non-Contact Heartbeat Detection Based on Ballistocardiograms Utilizing Physiological Information Guidance

Changzhe Jiao
Aoyu Yang
Hantao Zhao
Ruhan Yi
Shuiping Gou
Yu Sha
Wanshun Wen
Licheng Jiao

Ballistocardiograms (BCG) is a passive, non-contact heart rate detection technology that requires no action on the part of the individual. However, during the BCG signal acquisition process, the surface pressure generated by cardiac contraction is easily disturbed by external factors, and as people's health deteriorates, the j-peak (the main peak of the BCG signal) is no longer prominent. Our aim is to establish a non-contact, self-supervised heart rate detection method based on physiological information, to improve the accuracy and robustness of BCG heart rate detection under wider and more adverse conditions. The algorithm is guided by the heart rate estimation based on BCG itself, thereby reconstructing a signal with physiological significance. We also propose a heartbeat mapping algorithm based on Bidirectional Long Short-Term Memory Network (BiLSTM) for extracting global deep features, achieving real-time heartbeat prediction, and eliminating local deviations brought about by reconstruction. To verify the effectiveness of the proposed method, this paper evaluated 40 young subjects and 4 elderly subjects. Compared with the existing state-of-the-art methods, beat-to-beat heart rate estimation and heartbeat detection both performed excellently, surpassing most methods using precise labels. The experimental results show that the proposed method achieves effective heartbeat detection, demonstrating robustness and effectiveness in the face of unavoidable noise and variations.

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JBHI Journal 2024 Journal Article

Fully Automatic Fine-Grained Grading of Lumbar Intervertebral Disc Degeneration Using Regional Feature Recalibration Network

Nuo Tong
Shuiping Gou
Yulin Yang
Bo Liu
Yufeng Bai
Jingzhong Liu
Tan Ding

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential for the diagnosis and treatment design of high-incidence low back pain. However, the grading accuracy is still challenged by lacking the fine-grained degenerative details, which is mainly due to the existing grading methods are easily dominated by the salient nucleus pulposus regions in LIVD, overlooking the inconspicuous degeneration changes of the surrounding structures. In this study, a novel regional feature recalibration network (RFRecNet) is proposed to achieve accurate and reliable LIVD degeneration grading. Detection transformer (DETR) is first utilized to detect all LIVDs and then input to the proposed RFRecNet for the fine-grained grading. To obtain sufficient features from both the salient nucleus pulposus and the surrounding regions, a regional cube-based feature boosting and suppression (RC-FBS) module is designed to adaptively recalibrate the feature extraction and utilization from the various regions in LIVD, and a feature diversification (FD) module is proposed to capture the complementary semantic information from the multi-scale features for the comprehensive fine-grained degeneration grading. Extensive experiments were conducted on a clinically collected dataset, which consists of 500 MR scans with a total of 10225 LIVDs. An average grading accuracy of 90. 5%, specificity of 97. 5%, sensitivity of 90. 8%, and Cohen's kappa correlation coefficient of 0. 876 are obtained, which indicate that the proposed framework is promising to provide doctors with reliable and consistent fine-grained quantitative evaluation results of the LIVD degeneration conditions for the optimal surgical plan design.

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NeurIPS Conference 2023 Conference Paper

RGMIL: Guide Your Multiple-Instance Learning Model with Regressor

Zhaolong Du
Shasha Mao
Yimeng Zhang
Shuiping Gou
Licheng Jiao
Lin Xiong

In video analysis, an important challenge is insufficient annotated data due to the rare occurrence of the critical patterns, and we need to provide discriminative frame-level representation with limited annotation in some applications. Multiple Instance Learning (MIL) is suitable for this scenario. However, many MIL models paid attention to analyzing the relationships between instance representations and aggregating them, but neglecting the critical information from the MIL problem itself, which causes difficultly achieving ideal instance-level performance compared with the supervised model. To address this issue, we propose the $\textbf{\textit{Regressor-Guided MIL network} (RGMIL)}$, which effectively produces discriminative instance-level representations in a general multi-classification scenario. In the proposed method, we make full use of the $\textit{regressor}$ through our newly introduced $\textit{aggregator}$, $\textbf{\textit{Regressor-Guided Pooling} (RGP)}$. RGP focuses on simulating the correct inference process of humans while facing similar problems without introducing new parameters, and the MIL problem can be accurately described through the critical information from the $\textit{regressor}$ in our method. In experiments, RGP shows dominance on more than 20 MIL benchmark datasets, with the average bag-level classification accuracy close to 1. We also perform a series of comprehensive experiments on the MMNIST dataset. Experimental results illustrate that our $\textit{aggregator}$ outperforms existing methods under different challenging circumstances. Instance-level predictions are even possible under the guidance of RGP information table in a long sequence. RGMIL also presents comparable instance-level performance with S-O-T-A supervised models in complicated applications. Statistical results demonstrate the assumption that a MIL model can compete with a supervised model at the instance level, as long as a structure that accurately describes the MIL problem is provided. The codes are available on $\url{https: //github. com/LMBDA-design/RGMIL}$.

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EAAI Journal 2022 Journal Article

A multi-task learning for cavitation detection and cavitation intensity recognition of valve acoustic signals

Yu Sha
Johannes Faber
Shuiping Gou
Bo Liu
Wei Li
Stefan Schramm
Horst Stoecker
Thomas Steckenreiter

With the rapid development of smart manufacturing, data-driven machinery health management has received a growing attention. As one of the most popular methods in machinery health management, deep learning (DL) has achieved remarkable successes. However, due to the issues of limited samples and poor separability of different cavitation states of acoustic signals, which greatly hinder the eventual performance of DL modes for cavitation intensity recognition and cavitation detection. Also different tasks were performed separately conventionally. In this work, a novel multi-task learning framework for simultaneous cavitation detection and cavitation intensity recognition framework using 1-D double hierarchical residual networks (1-D DHRN) is proposed for analyzing valves acoustic signals. Firstly, a data augmentation method based on sliding window with fast Fourier transform (Swin-FFT) is developed to alleviate the small-sample issue confronted in this study. Secondly, a 1-D double hierarchical residual block (1-D DHRB) is constructed to capture sensitive features from the frequency domain acoustic signals of valve. Then, a new structure of 1-D DHRN is proposed. Finally, the devised 1-D DHRN is evaluated on two datasets of valve acoustic signals without noise ( Dataset 1 and Dataset 2 ) and one dataset of valve acoustic signals with realistic surrounding noise ( Dataset 3 ) provided by SAMSON AG (Frankfurt). Our method has achieved state-of-the-art results. The prediction accuracies of 1-D DHRN for cavitation intensitys recognition are as high as 93. 75%, 94. 31% and 100%, which indicates that 1-D DHRN outperforms other DL models and conventional methods. At the same time, the testing accuracies of 1-D DHRN for cavitation detection are as high as 97. 02%, 97. 64% and 100%. In addition, 1-D DHRN has also been tested for different frequencies of samples and shows excellent results for frequency of samples that mobile phones can accommodate.

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JBHI Journal 2021 Journal Article

Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression

Changzhe Jiao
Chao Chen
Shuiping Gou
Dong Hai
Bo-Yu Su
Marjorie Skubic
Licheng Jiao
Alina Zare

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise ( e. g. , sensor noise) and perturbations ( e. g. , body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.

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