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Tianqi Xu

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

CNM-UNet: Continuous Ordinary Differential Equations for Medical Image Segmentation

Tianqi Xu
Yashi Zhu
Quansong He
Yue Cao
Kaishen Wang
Zhang Yi
Tao He

Integrating Ordinary Differential Equations (ODEs) with U-shaped neural networks has emerged as a novel direction in medical image segmentation. Current networks predominantly employ discretization methods incorporating ODEs. However, these methods face inherent trade-offs between model compactness, computational accuracy, and efficiency. Continuous ODE solutions were rarely studied because they face three limitations: high computational costs, long training time, and poor generalization ability. To address these limitations, we propose an innovative Continuous Neural Memory ODE UNet (CNM-UNet), which replaces all hierarchical decoder layers in vanilla UNet with a single Continuous Neural Memory ODEs Block (CNM-Block) decoder, significantly reducing computation costs and improving training efficiency. CNM-UNet leverages ODEs' dynamic properties to establish continuous temporal feature extraction. For alleviating the generalization problem, a DUal SElf-updated (DUSE) strategy based on test-time adaptation principles is introduced to enhance cross-domain generalization. Experimental results demonstrate CNM-UNet's comprehensive advantages in computational capacity, convergence speed, and cross-domain adaptability, offering new insights for practical deployment of continuous ODE methodologies for medical image segmentation.

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ICLR Conference 2025 Conference Paper

EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents

Junting Chen
Checheng Yu
Xunzhe Zhou
Tianqi Xu
Yao Mu 0001
Mengkang Hu
Wenqi Shao
Yikai Wang

Heterogeneous multi-robot systems (HMRS) have emerged as a powerful ap- proach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based multi-agent systems (LLM-based MAS) have shown success in areas like software development and operating systems, but applying these systems to robot control presents unique challenges. In particular, the ca- pabilities of each agent in a multi-robot system are inherently tied to the physical composition of the robots, rather than predefined roles. To address this issue, we introduce a novel multi-agent framework designed to enable effective collab- oration among heterogeneous robots with varying embodiments and capabilities, along with a new benchmark named Habitat-MAS. One of our key designs is Robot Resume: Instead of adopting human-designed role play, we propose a self- prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities to guide their behavior in task planning and action execution. The Habitat-MAS bench- mark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning, which includes 1) manipulation, 2) perception, 3) navigation, and 4) comprehensive multi-floor object rearrangement. The experi- mental results indicate that the robot’s resume and the hierarchical design of our multi-agent system are essential for the effective operation of the heterogeneous multi-robot system within this intricate problem context.

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AAAI Conference 2025 Conference Paper

Uncertainty-aware Knowledge Tracing

Weihua Cheng
Hanwen Du
Chunxiao Li
Ersheng Ni
Liangdi Tan
Tianqi Xu
Yongxin Ni

Knowledge Tracing (KT) is crucial in education assessment, which focuses on depicting students' learning states and assessing students' mastery of subjects. With the rise of modern online learning platforms, particularly massive open online courses (MOOCs), an abundance of interaction data has greatly advanced the development of the KT technology. Previous research commonly adopts deterministic representation to capture students' knowledge states, which neglects the uncertainty during student interactions and thus fails to model the true knowledge state in learning process. In light of this, we propose an Uncertainty-Aware Knowledge Tracing model (UKT) which employs stochastic distribution embeddings to represent the uncertainty in student interactions, with a Wasserstein self-attention mechanism designed to capture the transition of state distribution in student learning behaviors. Additionally, we introduce the aleatory uncertainty-aware contrastive learning loss, which strengthens the model's robustness towards different types of uncertainties. Extensive experiments on six real-world datasets demonstrate that UKT not only significantly surpasses existing deep learning-based models in KT prediction, but also shows unique advantages in handling the uncertainty of student interactions.

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JBHI Journal 2020 Journal Article

Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks

Siyuan Zhang
Shan Wu
Shaoqiang Shang
Xuewei Qin
Xin Jia
Dapeng Li
Zhiwei Cui
Tianqi Xu

Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the low intrinsic contrast between the thermal lesion and normal tissue. Deep learning, especially convolutional neural network (CNN), has shown significant improvements in medical image analysis. Here, we propose and evaluate an US imaging based on a CNN architecture for the detection and monitoring of thermal lesions induced by MWA in porcine livers. Unlike dealing with images in many visual object recognition tasks, US radiofrequency (RF) data backscattered from the ablated region were utilized to capture features related to the thermal lesion. The dataset comprised of 1640 US RF envelope data matrices and their corresponding gross-pathology images, and were utilized for training and testing. After envelope detection, US B-mode, segmentation results based on CNN (SI CNN ), and modified CNN (SI m-CNN ) for US data were simultaneously reconstructed to reveal the suitability for monitoring of MWA. The SI CNN and SI m-CNN outperformed B-mode images for the detection and monitoring of MWA-induced thermal lesions. The values of the area under the receiver operating characteristic curve were 0. 8728 and 0. 8948 for the SI CNN and Si m-CNN, respectively, which were both higher than the value of 0. 6904 for B-mode images. Ablated regions that were assessed using SI m-CNN showed a good correlation (J 0. 8845, r 0. 8739, and E 0. 410) to gross-pathology images. This study was the first to illustrate that SI m-CNN has the potential to detect and monitor thermal lesions, and may be utilized as an alternative modality for image-guided MWA treatments.

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