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Chongyang Ding

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3 papers

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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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EAAI Journal 2022 Journal Article

Temporal segment graph convolutional networks for skeleton-based action recognition

Chongyang Ding
Shan Wen
Wenwen Ding
Kai Liu
Evgeny Belyaev

Different actions usually emphasize on different parts of a skeleton, even for a specific action, different action stages have the corresponding emphases. Previous studies generally construct the human skeletons as predefined, thus lacking the adaptability to different action modes. In addition, these methods simply employ the padding or truncation operation on the skeleton sequence to fix the sequence length, resulting in additional temporal misalignment problem. In this work, we propose a novel temporal segment graph convolutional networks (TS-GCN) for skeleton-based action recognition. Our model divides the whole sequence into several subsequences. Then GCNs are applied on each subsequence to capture the dynamic information stage by stage, which can align the motion features in temporal domain. Besides, in order to explore the intrinsic features contained in each subsequence, our model introduces a graph-adaptive method to construct an individual graph that can be learned and updated from skeleton data for each subsequence, which increases the generality of graph construction to adapt to different sequences. Extensive experiments are conducted on two standard datasets, NTU-RGB+D and Kinetics. The experimental results demonstrate the effectiveness of the proposed method.

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AAAI Conference 2021 Conference Paper

Spatio-Temporal Difference Descriptor for Skeleton-Based Action Recognition

Chongyang Ding
Kai Liu
Jari Korhonen
Evgeny Belyaev

In skeletal representation, intra-frame differences between body joints, as well as inter-frame dynamics between body skeletons contain discriminative information for action recognition. Conventional methods for modeling human skeleton sequences generally depend on motion trajectory and body joint dependency information, thus lacking the ability to identify the inherent differences of human skeletons. In this paper, we propose a spatio-temporal difference descriptor based on a directional convolution architecture that enables us to learn the spatio-temporal differences and contextual dependencies between different body joints simultaneously. The overall model is built on a deep symmetric positive definite (SPD) metric learning architecture designed to learn discriminative manifold features with the well-designed non-linear mapping operation. Experiments on several action datasets show that our proposed method achieves up to 3% accuracy improvement over state-of-the-art methods.

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