Author name cluster

Licheng Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

JBHI Journal 2026 Journal Article

DF-DiffVSR: Deformable Field-Driven Diffusion Model for Inter-Slice Continuity Enhancement in Medical Volume Super-Resolution

Can Wang
Min Liu
Qinghao Liu
Yuehao Zhu
Xiang Chen
Licheng Liu
Yaonan Wang
Erik Meijering

Medical volumetric imaging is crucial for precise diagnosis, but limited by equipment and acquisition constraints, anisotropic resolution leads to challenges in detecting small lesions and 3D visualization. While volumetric super-resolution methods can mitigate this issue, existing techniques suffer from limited receptive fields, failing to fully exploit inter-slice correlations and resulting in compromised inter-slice continuity. To address this limitation, we propose DF-DiffVSR, a novel deformable field-enhanced diffusion model for medical volume super resolution. The proposed method integrates optical flow principles with diffusion models through a Deformable Field Extraction (DFE) module, which explicitly learns inter slice motion information to enhance structural continuity in the through-plane direction. Furthermore, we design a Multiscale Large Kernel Convolution (MLKC) module that employs striped convolutions with varying kernel sizes to expand the receptive field and capture global anatomical context. Evaluated on RPLHR-CT and IXI-T2 datasets, DF DiffVSR achieves state-of-the-art (SOTA) performance, surpassing the sub-optimal method by 0. 732 dB and 0. 214 dB in PSNR, respectively, demonstrating superior capabilities in preserving inter-slice continuity and recovering fine grained details.

Details DOI

NeurIPS Conference 2025 Conference Paper

Online Prediction with Limited Selectivity

Licheng Liu
Mingda Qiao

Selective prediction [Dru13, QV19] models the scenario where a forecaster freely decides on the prediction window that their forecast spans. Many data statistics can be predicted to a non-trivial error rate without any distributional assumptions or expert advice, yet these results rely on that the forecaster may predict at any time. We introduce a model of Prediction with Limited Selectivity (PLS) where the forecaster can start the prediction only on a subset of the time horizon. We study the optimal prediction error both on an instance-by-instance basis and via an average-case analysis. We introduce a complexity measure that gives instance-dependent bounds on the optimal error. For a randomly-generated PLS instance, these bounds match with high probability.

PDF Details

AAAI Conference 2023 Conference Paper

Physics Guided Neural Networks for Time-Aware Fairness: An Application in Crop Yield Prediction

Erhu He
Yiqun Xie
Licheng Liu
Weiye Chen
Zhenong Jin
Xiaowei Jia

This paper proposes a physics-guided neural network model to predict crop yield and maintain the fairness over space. Failures to preserve the spatial fairness in predicted maps of crop yields can result in biased policies and intervention strategies in the distribution of assistance or subsidies in supporting individuals at risk. Existing methods for fairness enforcement are not designed for capturing the complex physical processes that underlie the crop growing process, and thus are unable to produce good predictions over large regions under different weather conditions and soil properties. More importantly, the fairness is often degraded when existing methods are applied to different years due to the change of weather conditions and farming practices. To address these issues, we propose a physics-guided neural network model, which leverages the physical knowledge from existing physics-based models to guide the extraction of representative physical information and discover the temporal data shift across years. In particular, we use a reweighting strategy to discover the relationship between training years and testing years using the physics-aware representation. Then the physics-guided neural network will be refined via a bi-level optimization process based on the reweighted fairness objective. The proposed method has been evaluated using real county-level crop yield data and simulated data produced by a physics-based model. The results demonstrate that this method can significantly improve the predictive performance and preserve the spatial fairness when generalized to different years.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

Zhexiong Liu
Licheng Liu
Yiqun Xie
Zhenong Jin
Xiaowei Jia

Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in model adaptation with small samples, existing meta learning methods remain limited in handling a large number of heterogeneous tasks, e.g., a large number of locations with varying data patterns. To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that transforms regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.

PDF Details DOI