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Yikun Zhang

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8 papers

2 author rows

JBHI Journal 2026 Journal Article

WOADNet: A Wavelet-Inspired Orientational Adaptive Dictionary Network for CT Metal Artifact Reduction

Tong Jin
Jin Liu
Diandian Wang
Kun Wang
Chenlong Miao
Yikun Zhang
Dianlin Hu
Zhan Wu

In computed tomography (CT), metal artifacts pose a persistent challenge to achieving high-quality imaging. Despite advancements in metal artifact reduction (MAR) techniques, many existing approaches have not fully leveraged the intrinsic a priori knowledge related to metal artifacts, improved model interpretability, or addressed the complex texture of CT images effectively. To address these limitations, we propose a novel and interpretable framework, the wavelet-inspired oriented adaptive dictionary network (WOADNet). WOADNet builds on sparse coding with orientational information in the wavelet domain. By exploring the discriminative features of artifacts and anatomical tissues, we adopt a high-precision filter parameterization strategy that incorporates multiangle rotations. Furthermore, we integrate a reweighted sparse constraint framework into the convolutional dictionary learning process and employ a cross-space, multiscale attention mechanism to construct an adaptive convolutional dictionary unit for the artifact feature encoder. This innovative design allows for flexible adjustment of weights and convolutional representations, resulting in significant image quality improvements. The experimental results using synthetic and clinical datasets demonstrate that WOADNet outperforms both traditional and state-of-the-art MAR methods in terms of suppressing artifacts.

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ICLR Conference 2025 Conference Paper

Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation

Yikun Zhang
Geyan Ye
Chaohao Yuan
Bo Han 0003
Long-Kai Huang
Jianhua Yao 0001
Wei Liu 0005
Yu Rong 0001

Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields. However, most approaches employ a global alignment approach to learn the knowledge from different modalities that may fail to capture fine-grained information, such as molecule-and-text fragments and stereoisomeric nuances, which is crucial for downstream tasks. Furthermore, it is incapable of modeling such information using a similar global alignment strategy due to the lack of annotations about the fine-grained fragments in the existing dataset. In this paper, we propose Atomas, a hierarchical molecular representation learning framework that jointly learns representations from SMILES strings and text. We design a Hierarchical Adaptive Alignment model to automatically learn the fine-grained fragment correspondence between two modalities and align these representations at three semantic levels. Atomas's end-to-end training framework supports understanding and generating molecules, enabling a wider range of downstream tasks. Atomas achieves superior performance across 12 tasks on 11 datasets, outperforming 11 baseline models thus highlighting the effectiveness and versatility of our method. Scaling experiments further demonstrate Atomas’s robustness and scalability. Moreover, visualization and qualitative analysis, validated by human experts, confirm the chemical relevance of our approach. Codes are released on ~\url{https://github.com/yikunpku/Atomas}.

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JBHI Journal 2025 Journal Article

EDG-Net: Encryption and Decryption based Gan-attention Network for CT images in the Internet of Medical Things and Telemedicine

Kai Chen
Yuchen Li
Shipeng Xie
Zhan Wu
Yikun Zhang
Jean-Louis Coatrieux
Wei Yan
Yang Chen

CT images provide medical practitioners with a scientific and intuitive rationale for the diagnosis of clinical diseases. The Internet of Medical Things (IoMT) and telemedicine facilitate the preservation, transmission, and application of medical data and drive the sharing of medical data, especially medical images. Encryption and decryption of CT images distributed in the IoMT and telemedicine are becoming critical because they contain a large amount of private patient–ensitive information and are vulnerable to third-party attacks, resulting in information exposure and privacy leakage. In this paper, we propose an Encryption and Decryption based Gan-attention network (EDG-Net) for CT images in the IoMT and telemedicine. EDG-Net consists of a generator, two discriminators, a domain transfer of attention, and adaptive normalization. In addition, a double encryption and decryption strategy is introduced by EDG-Net to effectively improve the security of the ciphertext image and the fidelity of the decrypted plaintext image. Specifically, during the encryption or decryption phase, the generator transforms the CT images mutually in the plaintext and ciphertext domains. Two discriminators to identify and modify the differences between these two domain transformations, especially improve the accuracy of the reconstruction during decryption. The parameters of the trained encryption and decryption network are considered as the secret keys of encryption and decryption. Qualitative and quantitative analysis of public and private datasets demonstrates the superior performance of EDG-Net regarding encryption security and robustness as well as decryption accuracy.

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IROS Conference 2025 Conference Paper

Safe Corridor-Based MPC for Follow-Ahead and Obstacle Avoidance of Mobile Robot in Cluttered Environments

Yikun Zhang
Xinxing Chen
Jian Huang 0001

In cluttered environments, a human-following mobile robot must predict the motion intention of the followed human and take environmental obstacles into consideration. Consequently, it brings several challenges, such as the human’s detour direction prediction problem and the visibility maintenance problem for route planning. To overcome these problems, this paper proposes an integrated follow-ahead framework, in which the human’s detour behavior is predicted by the Leg Motion Model-based EKF (LMM-EKF) and the iterative human route search algorithm, followed by the Safe Corridor-based Model Predictive Controller (SCMPC) used to obtain the optimal control solution. Also, a new perspective about visibility is provided in this paper that, via placing multiple obstacle-free safe regions along the human’s intended direction without any complex preprocessing for the point cloud, SCMPC prevents the robot from collision and occlusion simultaneously based on the basic properties of the convex set. The validity of the proposed method is comprehensively verified through real-world experiments.

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JBHI Journal 2023 Journal Article

DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction

Yikun Zhang
Dianlin Hu
Shilei Hao
Jin Liu
Guotao Quan
Yi Zhang
Xu Ji
Yang Chen

Sparse-view Computed Tomography (CT) has the ability to reduce radiation dose and shorten the scan time, while the severe streak artifacts will compromise anatomical information. How to reconstruct high-quality images from sparsely sampled projections is a challenging ill-posed problem. In this context, we propose the unrolled Deep Residual Error iterAtive Minimization Network (DREAM-Net) based on a novel iterative reconstruction framework to synergize the merits of deep learning and iterative reconstruction. DREAM-Net performs constraints using deep neural networks in the projection domain, residual space, and image domain simultaneously, which is different from the routine practice in deep iterative reconstruction frameworks. First, a projection inpainting module completes the missing views to fully explore the latent relationship between projection data and reconstructed images. Then, the residual awareness module attempts to estimate the accurate residual image after transforming the projection error into the image space. Finally, the image refinement module learns a non-standard regularizer to further fine-tune the intermediate image. There is no need to empirically adjust the weights of different terms in DREAM-Net because the hyper-parameters are embedded implicitly in network modules. Qualitative and quantitative results have demonstrated the promising performance of DREAM-Net in artifact removal and structural fidelity.

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JBHI Journal 2022 Journal Article

PRIOR: Prior-Regularized Iterative Optimization Reconstruction For 4D CBCT

Dianlin Hu
Yikun Zhang
Jin Liu
Yi Zhang
Jean Louis Coatrieux
Yang Chen

4D cone-beam computed tomography (CBCT) is an important imaging modality in image-guided radiation therapy to address the motion-induced artifacts caused by organ movements during the respiratory process. However, due to the extremely sparse projection data for each temporal phase, 4D CBCT reconstructions will suffer from severe streaking artifacts. Therefore, to tackle the streak artifacts and provide high-quality images, we proposed a framework termed Prior-Regularized Iterative Optimization Reconstruction (PRIOR) for 4D CBCT. The PRIOR framework combines the physics-based model and data-driven method simultaneously, with powerful feature extracting capacity, significantly promoting the image quality compared to single model-based or deep learning-based methods. Besides, we designed a specialized deep learning model named PRIOR-Net, which can effectively excavate the static information in the prior image reconstructed from the fully-sampled projections at the encoding stage to improve the reconstruction performance for individual phase-resolved images. Both the simulated and clinical 4D CBCT datasets were performed to evaluate the performance of the PRIOR-Net and the PRIOR framework. Compared with the advanced 4D CBCT reconstruction methods, the proposed methods achieve promising results quantitatively and qualitatively in streak artifact suppression, soft tissue restoration, and tiny detail preservation.

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JMLR Journal 2021 Journal Article

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data

Yikun Zhang
Yen-Chi Chen

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of the directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of the algorithm, we evaluate it as a mode clustering method on both simulated and real-world data sets. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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AAAI Conference 2018 Short Paper

Bayesian Network Structure Learning: The Two-Step Clustering-Based Algorithm

Yikun Zhang
Jiming Liu
Yang Liu

In this paper we introduce a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time ef- ﬁciency of state-of-the-art algorithms for Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the ﬁrst step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the withincluster arcs being well preserved, we learn the betweencluster structure of the given network. Experimental results on benchmark datasets show that a wide range of structure learning algorithms beneﬁt from the proposed clusteringbased strategy in terms of both accuracy and efﬁciency.

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