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Kang Chen

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19 papers

2 author rows

EAAI Journal 2026 Journal Article

Curvature effects on wearable ultrasound image quality and generalized deep learning correction methods

Shengrong Lin
Kang Chen
Jianlin Yang
Jianming Wen
Dexing Kong

A fundamental challenge in wearable ultrasound is phase aberration caused by transducer deformation, which substantially degrades image quality. This work systematically evaluates curvature effects across four standard imaging modalities and presents a generalized deep learning framework that effectively performs phase corrections for all modalities. Results were demonstrated on the ultrasound phantom and in-vivo data (three participants). Skin curvature measurements revealed characteristic radii of ∼60–160 mm, within which all modalities showed significant image degradation. Transducer element reduction is found to be effective for easing phase correction. Using the same dataset resizing protocol, the deep learning models (U-Net and Pix2Pix) for every imaging modality were trained with the same procedure. Comparative analysis shows that phased-array imaging is found to be more resilient to curvature artifacts considering both imaging resolution and contrast; while Pix2Pix excelled at resolution improvement, U-Net proved superior for contrast enhancement and general in-vivo application; phased-array imaging is effective for wide-field cardiac imaging, and plane-wave compounding/focused-wave imaging with a U-Net model is suitable for imaging superficial structures like the carotid artery. These results establish practical guidelines for clinical implementation of wearable ultrasound.

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IROS Conference 2025 Conference Paper

A Multi-modal Hand Imitation Dataset for Dexterous Hand

Shaochen Wang
Qilin Wu
Kang Chen
Qing Huang
Zhuo Cheng
Beihao Xia

Multimodal data is indispensable for advancing imitation learning, particularly in the context of dexterous hands. However, existing datasets predominantly rely on single-modality inputs, such as RGB images, which inherently lack the capacity to capture the spatial and temporal dynamics essential for achieving human-like dexterity. To address this limitation, we introduce Multi-Modal Dex, a dataset that integrates multimodal sensory data to enable the effective learning of dexterous skills from human demonstrations. By combining visual, point cloud, and kinematic modalities, our dataset provides a richer representation of hand interactions, thereby facilitating a more nuanced understanding of dexterous imitation. Our framework leverages neural rendering and kinematic optimization to align human and robotic hand poses in a shared canonical space, enabling geometrically consistent skill transfer. Furthermore, we analyze the dataset’s potential to advance dexterous robots in perception, imitation learning, and real-world dexterous skill transfer. The data is available at https://github.com/WangShaoSUN/MutliDex.

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ICML Conference 2025 Conference Paper

Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation

Zecheng Hao
Qichao Ma
Kang Chen
Yi Zhang
Zhaofei Yu
Tiejun Huang 0003

Spiking Neural Network (SNN), as a brain-inspired and energy-efficient network, is currently facing the pivotal challenge of exploring a suitable and efficient learning framework. The predominant training methodologies, namely Spatial-Temporal Back-propagation (STBP) and ANN-SNN Conversion, are encumbered by substantial training overhead or pronounced inference latency, which impedes the advancement of SNNs in scaling to larger networks and navigating intricate application domains. In this work, we propose a novel parallel conversion learning framework, which establishes a mathematical mapping relationship between each time-step of the parallel spiking neurons and the cumulative spike firing rate. We theoretically validate the lossless and sorting properties of the conversion process, as well as pointing out the optimal shifting distance for each step. Furthermore, by integrating the above framework with the distribution-aware error calibration technique, we can achieve efficient conversion towards more general activation functions or training-free circumstance. Extensive experiments have confirmed the significant performance advantages of our method for various conversion cases under ultra-low time latency. To our best knowledge, this is the first work which jointly utilizes parallel spiking calculation and ANN-SNN Conversion, providing a highly promising approach for SNN supervised training. Code is available at https: //github. com/hzc1208/Parallel_Conversion.

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NeurIPS Conference 2025 Conference Paper

MigGPT: Harnessing Large Language Models for Automated Migration of Out-of-Tree Linux Kernel Patches Across Versions

Pucheng Dang
Di Huang
Dong Li
Kang Chen
Yuanbo Wen
Qi Guo
Xing Hu

Out-of-tree kernel patches are essential for adapting the Linux kernel to new hardware or enabling specific functionalities. Maintaining and updating these patches across different kernel versions demands significant effort from experienced engineers. Large language models (LLMs) have shown remarkable progress across various domains, suggesting their potential for automating out-of-tree kernel patch migration. However, our findings reveal that LLMs, while promising, struggle with incomplete code context understanding and inaccurate migration point identification. In this work, we propose MigGPT, a framework that employs a novel code fingerprint structure to retain code snippet information and incorporates three meticulously designed modules to improve the migration accuracy and efficiency of out-of-tree kernel patches. Furthermore, we establish a robust benchmark using real-world out-of-tree kernel patch projects to evaluate LLM capabilities. Evaluations show that MigGPT significantly outperforms the direct application of vanilla LLMs, achieving an average completion rate of 72. 59\% ($\uparrow 50. 74\%$) for migration tasks.

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AAAI Conference 2025 Conference Paper

Rethinking High-speed Image Reconstruction Framework with Spike Camera

Kang Chen
Yajing Zheng
Tiejun Huang
Zhaofei Yu

Spike cameras, as innovative neuromorphic devices, generate continuous spike streams to capture high-speed scenes with lower bandwidth and higher dynamic range than traditional RGB cameras. However, reconstructing high-quality images from the spike input under low-light conditions remains challenging. Conventional learning-based methods often rely on the synthetic dataset as the supervision for training. Still, these approaches falter when dealing with noisy spikes fired under the low-light environment, leading to further performance degradation in the real-world dataset. This phenomenon is primarily due to inadequate noise modelling and the domain gap between synthetic and real datasets, resulting in recovered images with unclear textures, excessive noise, and diminished brightness. To address these challenges, we introduce a novel spike-to-image reconstruction framework SpikeCLIP that goes beyond traditional training paradigms. Leveraging the CLIP model's powerful capability to align text and images, we incorporate the textual description of the captured scene and unpaired high-quality datasets as the supervision. Textual descriptions provide additional context that guides the network's feature reconstruction, while high-quality datasets help produce sharp latent images. Our experiments on real-world low-light datasets U-CALTECH and U-CIFAR demonstrate that SpikeCLIP significantly enhances texture details and the luminance balance of recovered images. Furthermore, the reconstructed images are well-aligned with the broader visual features needed for downstream tasks, ensuring more robust and versatile performance in challenging environments.

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IJCAI Conference 2025 Conference Paper

SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos

Wenxuan Liu
Yao Deng
Kang Chen
Xian Zhong
Zhaofei Yu
Tiejun Huang

Existing saliency detection methods struggle in real-world scenarios due to motion blur and occlusions. In contrast, spike cameras, with their high temporal resolution, significantly enhance visual saliency maps. However, the composite noise inherent to spike camera imaging introduces discontinuities in saliency detection. Low-quality samples further distort model predictions, leading to saliency bias. To address these challenges, we propose Spike-navigated Optimal TrAnsport Saliency Region Detection (SOTA), a framework that leverages the strengths of spike cameras while mitigating biases in both spatial and temporal dimensions. Our method introduces Spike-based Micro-debias (SM) to capture subtle frame-to-frame variations and preserve critical details, even under minimal scene or lighting changes. Additionally, Spike-based Global-debias (SG) refines predictions by reducing inconsistencies across diverse conditions. Extensive experiments on real and synthetic datasets demonstrate that SOTA outperforms existing methods by eliminating composite noise bias. Our code and dataset will be released at https: //github. com/lwxfight/sota.

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YNIMG Journal 2025 Journal Article

Synthetizing SWI from 3T to 7T by generative diffusion network for deep medullary veins visualization

Sui Li
Xingguang Deng
Qiwei Li
Zhiming Zhen
Luyi Han
Kang Chen
Chaoyang Zhou
Fengxi Chen

Ultrahigh-field susceptibility-weighted imaging (SWI) provides excellent tissue contrast and anatomical details of brain. However, ultrahigh-field magnetic resonance (MR) scanner often expensive and provides uncomfortable noise experience for patient. Therefore, some deep learning approaches have been proposed to synthesis high-field MR images from low-filed MR images, most existing methods rely on generative adversarial network (GAN) and achieve acceptable results. While the dilemma in train process of GAN, generally recognized, limits the synthesis performance in SWI images for its microvascular structure. Diffusion models, as a promising alternative, indirectly characterize the gaussian noise to the target image with a slow sampling through a considerable number of steps. To address this limitation, we presented a generative diffusion-based deep learning imaging model, named conditional denoising diffusion probabilistic model (CDDPM), for synthesizing high-field (7 Tesla) SWI images form low-field (3 Tesla) SWI images and assess clinical applicability. Crucially, the experiment results demonstrate that the diffusion-based model that synthesizes 7T SWI from 3T SWI images is potentially to providing an alternative way to achieve the advantages of ultra-high field 7T MR images for deep medullary veins visualization.

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EAAI Journal 2025 Journal Article

Textual prompt guided image restoration

Qiuhai Yan
Aiwen Jiang
Kang Chen
Long Peng
Qiaosi Yi
Chunjie Zhang

Image restoration has been a cutting-edge topic in the academic and industrial fields of computer vision. Since degradation signals are often random and diverse, “all-in-one” models that can do blind image restoration have been concerned in recent years. Early works require training specialized headers and tails to handle each degradation of concern, which are manually cumbersome. Moreover, it lacks naturalness in practical application. Recent works focus on learning visual prompts from data distribution to identify degradation type. However, the prompts employed in most of models are non-text, lacking sufficient emphasis on the importance of human-in-the-loop. In this paper, an effective textual prompt guided image restoration model has been proposed. In this model, task-specific BERT (Bidirectional Encoder Representations from Transformers) is fine-tuned to accurately understand user’s instructions and generate textual prompt guidance. Depth-wise multi-head transposed attentions and gated convolution modules are designed to bridge the gap between textual prompts and visual features. The proposed model has innovatively introduced semantic prompts into low-level visual domain. It highlights the potential to provide a natural, precise, and controllable way to perform image restoration tasks. Extensive experiments have been done on public denoising, dehazing and deraining datasets. The experiment results demonstrate that, compared with popular state-of-the-art methods, the proposed model can obtain much more superior performance, achieving accurate recognition and removal of degradation without increasing model’s complexity. Related source codes and data will be publicly available on github site https: //github. com/MoTong-AI-studio/TextPromptIR.

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AAAI Conference 2025 Conference Paper

VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints

Xinyu Wang
Lei Liu
Kang Chen
Tao Han
Bin Li
Lei Bai

Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. We use two strategies to enhance long-term forecasting. (1) By enhancing the matching between TC intensity and spatial information, we can improve long-term forecasting performance. (2) Incorporating physical knowledge and physical constraints can help mitigate the accumulation of forecasting errors. To achieve the above strategies, we propose the VQLTI framework. VQLTI transfers the TC intensity information to a discrete latent space while retaining the spatial information differences, using large-scale spatial meteorological data as conditions. Furthermore, we leverage the forecast from the weather prediction model FengWu to provide additional physical knowledge for VQLTI. Additionally, we calculate the potential intensity (PI) to impose physical constraints on the latent variables. In the global long-term TC intensity forecasting, VQLTI achieves state-of-the-art results for the 24h to 120h, with the MSW (Maximum Sustained Wind) forecast error reduced by 35.65%-42.51% compared to ECMWF-IFS.

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JBHI Journal 2024 Journal Article

FBENet: Feature-Level Boosting Ensemble Network for Hashimoto's Thyroiditis Ultrasound Image Classification

Wenchao Jiang
Tianchun Luo
Zhipeng Liang
Kang Chen
Ji He
Zhiming Zhao
Jianxuan Wen
Ling Zhao

Distinguishing Hashimoto's thyroiditis (HT) lesions from ordinary thyroid tissues is difficult with ultrasound images. Challenges in achieving high performance of HT ultrasound image classification include the low resolution, blurred features and large area of irrelevant noise. To address these problems, we propose a Feature-level Boosting Ensemble Network (FBENet) for HT ultrasound image classification. Specifically, to capture the features of suspicious HT lesions efficiently, an Ensemble Feature Boosting Module (EFBM) is introduced into the feature-level ensemble to boost the blurred features. Then, the spatial attention mechanism is adopted in backbone models to improve the feature focusing performance and representation ability. Furthermore, feature-level ensemble technique is employed in the training process to achieve more comprehensive feature representation ability. Experimentally, FBENet was trained on 6, 503 HT ultrasound images, and tested on 1, 626 HT ultrasound images with 82. 92% accuracy and 89. 24% AUC on average.

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AIIM Journal 2024 Journal Article

FIT-graph: A multi-grained evolutionary graph based framework for disease diagnosis

Zizhu Liu
Qing Cao
Nan Du
Huizhen Shu
Erheng Zhong
Nan Jiang
Qiaoran Chen
Ying Shen

Early assessment, with the help of machine learning methods, can aid clinicians in optimizing the diagnosis and treatment process, allowing patients to receive critical treatment time. Due to the advantages of effective information organization and interpretable reasoning, knowledge graph-based methods have become one of the most widely used machine learning algorithms for this task. However, due to a lack of effective organization and use of multi-granularity and temporal information, current knowledge graph-based approaches are hard to fully and comprehensively exploit the information contained in medical records, restricting their capacity to make superior quality diagnoses. To address these challenges, we examine and study disease diagnosis applications in-depth, and propose a novel disease diagnosis framework named FIT-Graph. With novel medical multi-grained evolutionary graphs, FIT-Graph efficiently organizes the extracted information from various granularities and time stages, maximizing the retention of valuable information for disease inference and ensuring the comprehensiveness and validity of the final disease inference. We compare FIT-Graph with two real-world clinical datasets from cardiology and respiratory departments with the baseline. The experimental results show that its effect is better than the baseline model, and the baseline performance of the task is improved by about 5% in multiple indices.

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NeurIPS Conference 2024 Conference Paper

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation

Kun Chen
Peng Ye
Hao Chen
Kang Chen
Tao Han
Wanli Ouyang
Tao Chen
Lei Bai

Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the Fourier Neural Processes (FNP) for arbitrary-resolution data assimilation in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks. Code is available at https: //github. com/OpenEarthLab/FNP.

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JBHI Journal 2024 Journal Article

HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAM

Wenchao Jiang
Kang Chen
Zhipeng Liang
Tianchun Luo
Guanghui Yue
Zhiming Zhao
Wei Song
Ling Zhao

The early lesions of Hashimoto's thyroiditis are inconspicuous, and the ultrasonic features of these early lesions are indistinguishable from other thyroid diseases. This paper proposes a Hashimoto Thyroiditis ultrasound image classification model HT-RCM which consists of a Residual Full Convolution Transformer (Res-FCT) model and a Residual Channel Attention Module (Res-CAM). To collect the low-order information caused by hypoechoic signals accurately, the residual connection is injected between FCTs to form Res-FCT which helps HT-RCM superimpose the low-order input information and high-order output information together. Res-FCT can make HT-RCM focus more on hypoechoic information while avoiding gradient dispersion. The initial feature map is inserted into Res-FCT again through a down-sampling component, which further helps HT-RCM exact multi-level original semantic information in the ultrasound image. Res-CAM is constructed by implementing a residual connection between a channel attention module and a convolution layer. Res-CAM can effectively increase the weights of the lesion channels while suppressing the weights of the noise channels, which makes HT-RCM focus more on the lesion regions. The experimental results on our collected dataset show that HT-RCM outperforms the mainstream models and obtains state-of-the-art performance in HT ultrasound image classification.

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NeurIPS Conference 2024 Conference Paper

SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams

Kang Chen
Shiyan Chen
Jiyuan Zhang
Baoyue Zhang
Yajing Zheng
Tiejun Huang
Zhaofei Yu

Reconstructing a sequence of sharp images from the blurry input is crucial for enhancing our insights into the captured scene and poses a significant challenge due to the limited temporal features embedded in the image. Spike cameras, sampling at rates up to 40, 000 Hz, have proven effective in capturing motion features and beneficial for solving this ill-posed problem. Nonetheless, existing methods fall into the supervised learning paradigm, which suffers from notable performance degradation when applied to real-world scenarios that diverge from the synthetic training data domain. To address these challenges, we propose the first self-supervised framework for the task of spike-guided motion deblurring. Our approach begins with the formulation of a spike-guided deblurring model that explores the theoretical relationships among spike streams, blurry images, and their corresponding sharp sequences. We subsequently develop a self-supervised cascaded framework to alleviate the issues of spike noise and spatial-resolution mismatching encountered in the deblurring model. With knowledge distillation and re-blurring loss, we further design a lightweight deblur network to generate high-quality sequences with brightness and texture consistency with the original input. Quantitative and qualitative experiments conducted on our real-world and synthetic datasets with spikes validate the superior generalization of the proposed framework. Our code, data and trained models are available at \url{https: //github. com/chenkang455/S-SDM}.

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ICML Conference 2024 Conference Paper

Towards a Self-contained Data-driven Global Weather Forecasting Framework

Yi Xiao
Lei Bai 0001
Wei Xue 0003
Hao Chen 0045
Kun Chen 0004
Kang Chen
Tao Han 0002
Wanli Ouyang

Data-driven weather forecasting models are advancing rapidly, yet they rely on initial states (i. e. , analysis states) typically produced by traditional data assimilation algorithms. Four-dimensional variational assimilation (4DVar) is one of the most widely adopted data assimilation algorithms in numerical weather prediction centers; it is accurate but computationally expensive. In this paper, we aim to couple the AI forecasting model, FengWu, with 4DVar to build a self-contained data-driven global weather forecasting framework, FengWu-4DVar. To achieve this, we propose an AI-embedded 4DVar algorithm that includes three components: (1) a 4DVar objective function embedded with the FengWu forecasting model and its error representation to enhance efficiency and accuracy; (2) a spherical-harmonic-transform-based (SHT-based) approximation strategy for capturing the horizontal correlation of background error; and (3) an auto-differentiation (AD) scheme for determining the optimal analysis fields. Experimental results show that under the ERA5 simulated observational data with varying proportions and noise levels, FengWu-4DVar can generate accurate analysis fields; remarkably, it has achieved stable self-contained global weather forecasts for an entire year for the first time, demonstrating its potential for real-world applications. Additionally, our framework is approximately 100 times faster than the traditional 4DVar algorithm under similar experimental conditions, highlighting its significant computational efficiency.

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EAAI Journal 2023 Journal Article

A new hybrid prediction model of COVID-19 daily new case data

Guohui Li
Jin Lu
Kang Chen
Hong Yang

With the emergence of new mutant corona virus disease 2019 (COVID-19) strains such as Delta and Omicron, the number of infected people in various countries has reached a new high. Accurate prediction of the number of infected people is of far-reaching sig Nificance to epidemiological prevention in all countries of the world. In order to improve the prediction accuracy of COVID-19 daily new case data, a new hybrid prediction model of COVID-19 is proposed, which consists of four modules: decomposition, complexity judgment, prediction and error correction. Firstly, singular spectrum decomposition is used to decompose the COVID-19 data into singular spectrum components (SSC). Secondly, the complexity judgment is innovatively divided into high-complexity SSC and low-complexity SSC by neural network estimation time entropy. Thirdly, an improved LSSVM by GODLIKE optimization algorithm, named GLSSVM, is proposed to improve its prediction accuracy. Then, each low-complexity SSC is predicted by ARIMA, and each high-complexity SSC is predicted by GLSSVM, and the prediction error of each high-complexity SSC is predicted by GLSSVM. Finally, the predicted results are combined and reconstructed. Simulation experiments in Japan, Germany and Russia show that the proposed model has the highest prediction accuracy and the lowest prediction error. Diebold Mariano (DM) test is introduced to evaluate the model comprehensively. Taking Japan as an example, compared with ARIMA prediction model, the RMSE, average error and MAPE of the proposed model are reduced by 93. 17%, 91. 42% and 81. 20% respectively.

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AAAI Conference 2023 Conference Paper

CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics

Yiren Song
Xuning Shao
Kang Chen
Weidong Zhang
Zhongliang Jing
Minzhe Li

Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation. However, all existing works rely on additional generative models to ensure the quality of results, because CLIP alone cannot provide enough guidance information for fine-scale pixel-level changes. In this paper, we introduce CLIPVG, a text-guided image manipulation framework using differentiable vector graphics, which is also the first CLIP-based general image manipulation framework that does not require any additional generative models. We demonstrate that CLIPVG can not only achieve state-of-art performance in both semantic correctness and synthesis quality, but also is flexible enough to support various applications far beyond the capability of all existing methods.

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ICRA Conference 2023 Conference Paper

TODE-Trans: Transparent Object Depth Estimation with Transformer

Kang Chen
Shaochen Wang
Beihao Xia
Dongxu Li
Zhen Kan
Bin Li 0025

Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects due to the refraction and reflection of light. In this work, we present a transformer-based transparent object depth estimation approach from a single RGB-D input. We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas. In addition, to better enhance the fine-grained features, a feature fusion module (FFM) is designed to assist coherent prediction. Our empirical evidence demonstrates that our model delivers significant improvements in recent popular datasets, e. g. , 25% gain on RMSE and 21% gain on REL compared to previous state-of-the-art convolutional-based counterparts in ClearGrasp dataset. Extensive results show that our transformer-based model enables better aggregation of the object's RGB and inaccurate depth information to obtain a better depth representation. Our code and the pre-trained model are available at https://github.com/yuchendoudou/TODE.

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AIIM Journal 2021 Journal Article

Practical fine-grained learning based anomaly classification for ECG image

Qing Cao
Nan Du
Li Yu
Ming Zuo
Jingsheng Lin
Nathan Liu
Erheng Zhong
Zizhu Liu

As a widely used vital sign within cardiology, Electrocardiography (ECG) provides the basis for assessing heart function and diagnosing cardiovascular diseases. Automated anomaly detection for ECG plays an important role in improving patient diagnosis efficiency and reducing healthcare costs. Practically, due to the limits of electronics support or the medical system setting, image is a more common format for large-scale ECG storage in most clinical institutions. To guarantee an automated ECG detection model's scalability and practicality in clinical applications, taking good advantage of ECG images is crucial. However, existing time digital-based discriminative models fail to learn from images effectively for two reasons. First of all, the signals recorded on images have much lower resolution and higher noise, which makes it impractical to extract precise ECG signals following existing techniques. Meanwhile, the differences between abnormal signals are usually subtle, and they may be overwhelmed by the noises in the images as well. Towards this end, we design a novel neural framework that can be directly applied to massive ECG images determining various types of cardiology abnormalities. It classifies fine-grained ECG images based on weakly supervised strategy, in which case only image-level labeling is required. By eliminating the need for part annotations, the proposed method can result in significant savings in annotation time and cost. The effectiveness of the method is demonstrated by experimental results on two real ECG datasets.

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