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Lisheng Wu

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3 papers

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EAAI Journal 2025 Journal Article

Region-based weighting-and-enhancement network with adaptive class weighting loss for postoperative inguinal hernia prediction

Jiawei Zhang
Lisheng Wu
Qiang Fang
Weidong Yu
Zhengyu Hu
Fengyun Zhang
Cheng Yang
Xiaoqing Zhang

Postoperative inguinal hernia (PIH) is a common complication after radical prostatectomy, subsequently leading to multiple potential risks (e. g. , cardiovascular and cerebrovascular accidents) and increased surgical costs due to re-surgical reparation. Magnetic resonance imaging (MRI) examination is a widely used procedure before radical prostatectomy, which can investigate the muscle structures of the abdominal wall (MSAW). Recently, clinical studies have indicated that clinical parameters (e. g. , thickness and width of the external oblique muscle) of MSAW are strongly related to PIH. However, automated MRI-based PIH prediction based on deep neural networks has not been studied previously. Motivated by these observations, we propose a novel region-based weighting-and-enhancement network to predict PIH before radical prostatectomy based on MRI images automatically. Specifically, we employ the well-designed Region Weighting-and-Enhancement module to capture informative context representations through region weighting and regional context enhancement, by fully leveraging the potential of clinical MSAW priori. Additionally, this paper designs an effective adaptive class weighting loss to emphasize or suppress the samples with varying levels of significance to further boost the PIH prediction performance. The extensive experiments on a clinical MRI-PIH dataset and one publicly available MRI dataset manifest the superiority of our proposed methods over state-of-the-art deep neural networks and advanced loss methods.

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AAAI Conference 2020 Conference Paper

Learning to Communicate Implicitly by Actions

Zheng Tian
Shihao Zou
Ian Davies
Tim Warr
Lisheng Wu
Haitham Bou Ammar
Jun Wang

In situations where explicit communication is limited, human collaborators act by learning to: (i) infer meaning behind their partner’s actions, and (ii) convey private information about the state to their partner implicitly through actions. The ﬁrst component of this learning process has been well-studied in multi-agent systems, whereas the second — which is equally crucial for successful collaboration — has not. To mimic both components mentioned above, thereby completing the learning process, we introduce a novel algorithm: Policy Belief Learning (PBL). PBL uses a belief module to model the other agent’s private information and a policy module to form a distribution over actions informed by the belief module. Furthermore, to encourage communication by actions, we propose a novel auxiliary reward which incentivizes one agent to help its partner to make correct inferences about its private information. The auxiliary reward for communication is integrated into the learning of the policy module. We evaluate our approach on a set of environments including a matrix game, particle environment and the non-competitive bidding problem from contract bridge. We show empirically that this auxiliary reward is effective and easy to generalize. These results demonstrate that our PBL algorithm can produce strong pairs of agents in collaborative games where explicit communication is disabled.

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NeurIPS Conference 2019 Conference Paper

Multi-View Reinforcement Learning

Minne Li
Lisheng Wu
Jun Wang
Haitham Bou Ammar

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments.

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