Author name cluster

Kun Xie

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAAI Conference 2026 Conference Paper

PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generation

Kang Liu
Zhuoqi Ma
Zikang Fang
Yunan Li
Kun Xie
Qiguang Miao

Chest X-ray report generation aims to reduce radiologists' workload by automatically producing high-quality preliminary reports. A critical yet underexplored aspect of this task is the effective use of patient-specific prior knowledge---including clinical context (e.g., symptoms, medical history) and the most recent prior image---which radiologists routinely rely on for diagnostic reasoning. Most existing methods generate reports from single images, neglecting this essential prior information and thus failing to capture diagnostic intent or disease progression. To bridge this gap, we propose PriorRG, a novel chest X-ray report generation framework that emulates real-world clinical workflows via a two-stage training pipeline. In Stage 1, we introduce a prior-guided contrastive pre-training scheme that leverages clinical context to guide spatiotemporal feature extraction, allowing the model to align more closely with the intrinsic spatiotemporal semantics in radiology reports. In Stage 2, we present a prior-aware coarse-to-fine decoding for report generation that progressively integrates patient-specific prior knowledge with the vision encoder's hidden states. This decoding allows the model to align with diagnostic focus and track disease progression, thereby enhancing the clinical accuracy and fluency of the generated reports. Extensive experiments on MIMIC-CXR and MIMIC-ABN datasets demonstrate that PriorRG outperforms state-of-the-art methods, achieving a 3.6% BLEU-4 and 3.8% F1 score improvement on MIMIC-CXR, and a 5.9% BLEU-1 gain on MIMIC-ABN.

PDF Details DOI

JBHI Journal 2023 Journal Article

DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation

Ruiyang Li
Longchang Xu
Kun Xie
Jianfeng Song
Xiaowen Ma
Liang Chang
Qingsen Yan

Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learningbased algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some researchers have applied Transformer-based 3D networks to analyze medical images. However, the previous methods focus on modeling the local information (eg. edge) or global information (eg. morphology) with fixed network weights. To learn and extract complex tumor features of varied tumor size, location, and morphology for more accurate segmentation, we propose a Dynamic Hierarchical Transformer Network, named DHT-Net. The DHT-Net mainly contains a Dynamic Hierarchical Transformer (DHTrans) structure and an Edge Aggregation Block (EAB). The DHTrans first automatically senses the tumor location by Dynamic Adaptive Convolution, which employs hierarchical operations with the different receptive field sizes to learn the features of various tumors, thus enhancing the semantic representation ability of tumor features. Then, to adequately capture the irregular morphological features in the tumor region, DHTrans aggregates global and local texture information in a complementary manner. In addition, we introduce the EAB to extract detailed edge features in the shallow fine-grained details of the network, which provides sharp boundaries of liver and tumor regions. We evaluate DHT-Net on two challenging public datasets, LiTS and 3DIRCADb. The proposed method has shown superior liver and tumor segmentation performance compared to several state-of-the-art 2D, 3D, and 2. 5D hybrid models.

Details DOI

JBHI Journal 2021 Journal Article

Online MECG Compression Based on Incremental Tensor Decomposition for Wearable Devices

Ling Xiao
Qian Zhang
Kun Xie
Chunxia Xiao

Lightweight and real-time multi-lead electrocardiogram (MECG) compression on wearable devices is important and challenging for long-term health monitoring. To utilize all three kinds of correlations of MECG data simultaneously, we construct 3-order incremental tensor and formulate data compression problem as tensor decomposition. However, the conventional tensor decomposition algorithms for large-scale tensor are usually too computationally expensive to apply to wearable devices. To reduce the computation complexity, we develop online compression approach by incremental tracking the CANDECOMP/PARAFAC (CP) decomposition of dynamic incremental tensors, which can efficiently utilize the tensor compression result based on the previous MECG data to derive the tensor compression upon arriving of new data. We evaluate the performance of our method with the Physikalisch-Technische Bundesanstalt MECG diagnostic dataset. Our method can achieve the averaged percentage root-mean-square difference (PRD) of 8. 35% $\pm$ 2. 28% and the compression ratio (CR) of 43. 05 $\pm$ 2. 01, which is better than five state-of-the-art of methods. Additionally, it can also well preserve the information of R-peak. Our method is suitable for near real-time MECG compression on wearable devices.

Details DOI

AAAI Conference 2019 Conference Paper

Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting

Zulong Diao
Xin Wang
Dafang Zhang
Yingru Liu
Kun Xie
Shaoyao He

Graph convolutional neural networks (GCNN) have become an increasingly active field of research. It models the spatial dependencies of nodes in a graph with a pre-defined Laplacian matrix based on node distances. However, in many application scenarios, spatial dependencies change over time, and the use of fixed Laplacian matrix cannot capture the change. To track the spatial dependencies among traffic data, we propose a dynamic spatio-temporal GCNN for accurate traffic forecasting. The core of our deep learning framework is the finding of the change of Laplacian matrix with a dynamic Laplacian matrix estimator. To enable timely learning with a low complexity, we creatively incorporate tensor decomposition into the deep learning framework, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationship and a local component that captures the traffic fluctuations. We propose a novel design to estimate the dynamic Laplacian matrix of the graph with above two components based on our theoretical derivation, and introduce our design basis. The forecasting performance is evaluated with two realtime traffic datasets. Experiment results demonstrate that our network can achieve up to 25% accuracy improvement.

PDF Details