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Chong Jiang

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4 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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AAAI Conference 2020 Short Paper

Generative Adversarial Imitation Learning from Failed Experiences (Student Abstract)

Jiacheng Zhu
Jiahao Lin
Meng Wang
Yingfeng Chen
Changjie Fan
Chong Jiang
Zongzhang Zhang

Imitation learning provides a family of promising methods that learn policies from expert demonstrations directly. As a model-free and on-line imitation learning method, generative adversarial imitation learning (GAIL) generalizes well to unseen situations and can handle complex problems. In this paper, we propose a novel variant of GAIL called GAIL from failed experiences (GAILFE). GAILFE allows an agent to utilize failed experiences in the training process. Moreover, a constrained optimization objective is formalized in GAILFE to balance learning from given demonstrations and from self-generated failed experiences. Empirically, compared with GAIL, GAILFE can improve sample efﬁciency and learning speed over different tasks.

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AAAI Conference 2020 Short Paper

Third-Person Imitation Learning via Image Difference and Variational Discriminator Bottleneck (Student Abstract)

Chong Jiang
Zongzhang Zhang
Zixuan Chen
Jiacheng Zhu
Junpeng Jiang

Third-person imitation learning (TPIL) is a variant of generative adversarial imitation learning and can learn an expert-like policy from third-person expert demonstrations. Third-person expert demonstrations usually exist in the form of videos recorded in a third-person perspective, and there is a lack of direct correspondence with samples generated by agent. To alleviate this problem, we improve TPIL by applying image difference and variational discriminator bottleneck. Empirically, our new method has better performance than TPIL on two MuJoCo tasks, Reacher and Inverted Pendulum.

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NeurIPS Conference 2015 Conference Paper

Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits

Huasen Wu
R. Srikant
Xin Liu
Chong Jiang

We study contextual bandits with budget and time constraints under discrete contexts, referred to as constrained contextual bandits. The time and budget constraints significantly complicate the exploration and exploitation tradeoff because they introduce complex coupling among contexts over time. To gain insight, we first study unit-cost systems with known context distribution. When the expected rewards are known, we develop an approximation of the oracle, referred to Adaptive-Linear-Programming(ALP), which achieves near-optimality and only requires the ordering of expected rewards. With these highly desirable features, we then combine ALP with the upper-confidence-bound (UCB) method in the general case where the expected rewards are unknown a priori. We show that the proposed UCB-ALP algorithm achieves logarithmic regret except in certain boundary cases. Further, we design algorithms and obtain similar regret analysis results for more general systems with unknown context distribution or heterogeneous costs. To the best of our knowledge, this is the first work that shows how to achieve logarithmic regret in constrained contextual bandits. Moreover, this work also sheds light on the study of computationally efficient algorithms for general constrained contextual bandits.

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