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Wenxi Liu

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6 papers

2 author rows

JBHI Journal 2026 Journal Article

ProCausal-WS: Weakly Supervised Causal Representation Learning Driven Interpretable Prostate Cancer Diagnosis

Wencong Kong
Wenxi Liu
Hongyu Du

Current computational approaches to prostate cancer diagnosis rely on either linear causal models that cannot handle the nonlinear dependencies among imaging, genomic, and clinical variables, or on deep learning methods that demand exhaustive expert annotation and lack mechanisms for counterfactual reasoning. This work introduces ProCausal-WS, a weakly supervised causal representation learning framework that addresses both limitations simultaneously. It rests on three interlocking components: an invertible flow causal encoder that maps high-dimensional multimodal observations into a low-dimensional space of clinically interpretable causal factors through bijective transformations; an exogenous clinical intervention module that uses dynamic gating and structural equations to simulate treatment scenarios and generate controllable counterfactual predictions; and a weakly supervised alignment mechanism that combines contrastive learning with projection heads to constrain the semantic identifiability of the learned factors using only a small fraction of expert-labeled samples. On the TCGA-PRAD dataset, the framework achieves 92. 3% clinical causal concept identification accuracy while requiring only 8% complete annotations, and on the PANDA dataset, it reaches 89. 6% with 5% annotations. Intervention mean-squared error is reduced to 0. 018 on TCGA-PRAD, one-fourth that of the best baseline. Cross-dataset generalization yields an AUROC drop of no more than 0. 026 when transferring between institutions with different scanners and staining protocols. Expert pathologists rated 89. 6% of the generated counterfactual predictions as biologically plausible, and a longitudinal consistency analysis against real post-treatment biopsies confirms that the counterfactuals track actual disease trajectories rather than hallucinating visually convincing but causally unfaithful features.

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AAAI Conference 2026 Conference Paper

Seeing in Double: Dual-Granularity BEV Segmentation via Mamba-Driven Alignment and Polar-Decoupled Experts

Jiaxin Cai
Rui Lin
Jingze Su
Qi Li
Wenjie Yang
Yuanlong Yu
Wenxi Liu

Bird's Eye View (BEV) representation has become pivotal for autonomous driving, yet existing polar coordinate-based approaches face two critical limitations: (1) distant semantic misprojection caused by radial resolution decay, and (2) region-specific geometric distortions from non-uniform polar discretization. To address these issues, we propose a novel framework addressing these challenges through three key innovations. First, we present a bilateral heterogeneous network constructs multi-granularity BEV spaces, efficiently exploiting dual-resolution visual information for distant detail preservation. Second, we employ an align-fusion strategy for multi-granularity feature aggregation. Specifically, the Mamba-Based Cross-Resolution Alignment module establishes semantic consistency for perspective features through shared state-space optimization. In the later stage, the Adaptive BEV Space Selector dynamically aggregates multi-granularity BEV features. Third, we introduce a Mixture of Radial-Angular Decoupled Experts, which employs polar-aware expert routing to disentangle radial compression and angular shear distortions through specialized geometric refinement. Comprehensive experiments on nuScenes and Lyft L5 demonstrate the state-of-the-art performance of our model across various resolution settings, visibility filtering, and perception ranges.

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ICRA Conference 2024 Conference Paper

Efficient Semantic Segmentation for Compressed Video

Jiaxin Cai
Qi Li 0038
Yulin Shen
Jia Pan 0001
Wenxi Liu

Robots, constrained by limited onboard computing resources, often encounter situations wherein high-resolution and high-bit-rate videos captured by their cameras necessitate compression before further analysis. In this paper, we propose a novel video semantic segmentation paradigm for compressed video. Specifically, our framework draws the inspiration from the principle of Wavelet Transform, and thus we design the network structure, WTDecomNet, approximating the decomposition of high-resolution image into its low-resolution counterpart and axial details. The aim is to well preserve the image content through decomposition and maintain model efficiency by obtaining semantics from low-resolution image. To facilitate this purpose, we propose an efficient axial subband approximation module for extracting axial details and a lightweight temporal alignment module for associating keyframes and non-keyframes of compressed video. Through comprehensive experiments, we show that our model can achieve the state-of-the-art performance on public benchmarks. Especially on CamVid, comparing to baseline, our proposed model reduces the computational overhead by ∼70% while improving mIoU by ∼4%.

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IROS Conference 2021 Conference Paper

A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning

Lingping Gao
Jianchuan Ding
Wenxi Liu
Haiyin Piao
Yuxin Wang 0001
Xin Yang 0011
Baocai Yin

Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to apply on a large scale but also have poor robustness to irregular objects, e. g. , tables, chairs, shelves, etc. In this paper, we propose a vision-based collision avoidance framework to solve the challenging problem. Our method attempts to estimate the depth and incorporate the semantic information from RGB data to obtain a new form of data, pseudo-laser data, which combines the advantages of visual information and laser information. Compared to traditional laser data that only contains the one-dimensional distance information captured at a certain height, our proposed pseudo-laser data encodes the depth information and semantic information within the image, which makes our method more effective for irregular obstacles. Besides, we adaptively add noise to the laser data during the training stage to increase the robustness of our model in the real world, due to the estimated depth information is not accurate. Experimental results show that our framework achieves state-of-the-art performance in several unseen virtual and real-world scenarios.

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ICRA Conference 2020 Conference Paper

Learning Resilient Behaviors for Navigation Under Uncertainty

Tingxiang Fan
Pinxin Long
Wenxi Liu
Jia Pan 0001
Ruigang Yang
Dinesh Manocha

Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e. g. , autonomous driving). One of the reasons is that the learned policy cannot perform flexible and resilient behaviors as traditional methods to adapt to diverse environments. In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions. We present a novel approach for uncertainty-aware navigation by introducing an uncertainty-aware predictor to model the environmental uncertainty, and we propose a novel uncertainty-aware navigation network to learn resilient behaviors in the prior unknown environments. To train the proposed uncertainty-aware network more stably and efficiently, we present the temperature decay training paradigm, which balances exploration and exploitation during the training process. Our experimental evaluation demonstrates that our approach can learn resilient behaviors in diverse environments and generate adaptive trajectories according to environmental uncertainties.

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ICRA Conference 2018 Conference Paper

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Pinxin Long
Tingxiang Fan
Xinyi Liao
Wenxi Liu
Hao Zhang 0170
Jia Pan 0001

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca.

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