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Kai Xie

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4 papers

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EAAI Journal 2025 Journal Article

A weighted graph network-based method for combining conflicting evidence

Jinjian Lin
Kai Xie

Information fusion technology is crucial in intricate information systems, and Dempster–Shafer evidence(DSE) theory plays a significant role in it. However, most of the current research focuses on improving the conflict measurement method of high-conflict evidence in the DSE theory framework, while ignoring the comprehensive consideration of multiple conflicts of complex information. Considering the generality of graph network to complex system modeling, novel evidence measurement factors (EMF) and weighted Graph Convolution Network Dempster–Shafer evidence (wGCNDS) combination method, are proposed to optimize the combination of conflict evidence from the perspective of graph network. By constructing a weighted graph network, information transmission is realized and information fusion of associated nodes is completed. Numerical examples and real datasets verify the effectiveness and performance of wGCNDS.

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IJCAI Conference 2025 Conference Paper

Optimize Battery Control: A Multi-Objective Evolutionary Ensemble Reinforcement Learning Approach

Jingwei Hu
Kai Xie
Zheng Fang
Xiaodong Li
Junchi Yan
Zhihong Zhang

The Dynamically Reconfigurable Battery (DRB) systems, which use high-speed power electronic switches to dynamically adjust battery interconnections in real-time, are critical to the performance of the battery pack. Traditional battery management strategies often fail to address multi-objective optimization, leading to imbalanced performance and inadequate energy utilization. To enhance decision-making across multiple objectives, an Evolutionary Ensemble Reinforcement Learning (EERL) framework is proposed in this paper. This framework incorporates evolutionary algorithms to associate ensemble learning, thus improving reinforcement learning (RL) performance. It decomposes a complex objective into multiple sub-objectives, each optimized independently, while incorporating diverse performance metrics into the correlation stage to derive the Pareto optimal solution. The EERL can efficiently mitigate potential adverse effects such as short circuits, disconnections, and reverse charging, thereby effectively reducing capacity differences among various batteries. Simulations and real-world testing demonstrate that the proposed approach overcomes the issue of local optima entrapment in multi-objective optimization scenarios. In a real-world system, an 11. 08 % increase in energy efficiency is observed compared to existing approaches.

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JBHI Journal 2024 Journal Article

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in $^{18}$F-FDG PET Images Based on Multi-Deep-Learning Models

Chunjun Qian
Chong Jiang
Kai Xie
Chongyang Ding
Yue Teng
Jiawei Sun
Liugang Gao
Zhengyang Zhou

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in $^{18}$ F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0. 770, 95% CI: 0. 705-0. 834, with feature adding fusion method and C-index: 0. 764, 95% CI: 0. 695-0. 832, with feature concatenate fusion method) and OS prediction (C-index: 0. 770 (0. 692-0. 848) and 0. 771 (0. 694-0. 849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

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AAAI Conference 2020 Conference Paper

Segmenting Medical MRI via Recurrent Decoding Cell

Ying Wen
Kai Xie
Lianghua He

The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to memorize the long-term context information from the previous layers in the decoding phase. An encoder-decoder network, named Convolutional Recurrent Decoding Network (CRDN), is also proposed based on RDC for segmenting multi-modality medical MRI. CRDN adopts CNN backbone to encode image features and decode them hierarchically through a chain of RDCs to obtain the ﬁnal high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and HVSMR datasets demonstrate that the introduction of RDC effectively improves the segmentation accuracy as well as reduces the model size, and the proposed CRDN owns its robustness to image noise and intensity non-uniformity in medical MRI.

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