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Shaolei Liu

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3 papers

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AAAI Conference 2026 Conference Paper

Energy-guided Dual Domain-invariant Prompting Framework with Fourier Regularization for Generalized Few-Shot Medical Segmentation

Shaolei Liu
Yuting Wu
Dongchen Zhu
Jiamao Li

Precise segmentation of organ and tissue lesions is essential for clinical diagnosis and treatment. Despite the progress of deep learning and foundation segmentation models, their domain generalization capability remains limited particularly when dealing with cross-domain scenarios or unseen data, leading to significant performance degradation. Current medical SAM-based generalization methods face two primary challenges: First, existing prompt-tuning strategies inadequately capture key domain-invariant features; Second, the reliance on fully labeled source domain data is unrealistic in clinical practice. To address these challenges, we propose a novel Dual domain-Invariant Prompt Optimization (DIPO) enhanced by energy-guided augmentation and frequency consistency regularization for few-shot medical image segmentation generalization. Our approach introduces a multi-band momentum enhancement strategy to dynamically augment source data by leveraging diverse frequency bands of the Fourier amplitude spectrum. Furthermore, we integrate multiscale geometric representation-based non-subsampled shearlet transform and text prompts to strengthen the extraction of shape- and texture-related domain-invariant features. Finally, we employ frequency consistency regularization to refine model robustness using predictions from unlabeled data. Experimental results in prostate and fundus datasets demonstrate that our method significantly outperforms current state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

Reducing Domain Gap in Frequency and Spatial Domain for Cross-Modality Domain Adaptation on Medical Image Segmentation

Shaolei Liu
Siqi Yin
Linhao Qu
Manning Wang

Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing UDA methods depend on adversarial learning to address the domain gap between different image modalities, which is ineffective due to its complicated training process. In this paper, we propose a simple yet effective UDA method based on frequency and spatial domain transfer under multi-teacher distillation framework. In the frequency domain, we first introduce non-subsampled contourlet transform for identifying domain-invariant and domain-variant frequency components (DIFs and DVFs), and then keep the DIFs unchanged while replacing the DVFs of the source domain images with that of the target domain images to narrow the domain gap. In the spatial domain, we propose a batch momentum update-based histogram matching strategy to reduce the domain-variant image style bias. Experiments on two commonly used cross-modality medical image segmentation datasets show that our proposed method achieves superior performance compared to state-of-the-art methods.

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AAAI Conference 2022 Conference Paper

TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning

Linhao Qu
Shaolei Liu
Manning Wang
Zhijian Song

In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses selfsupervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features. In addition, to compensate for the defect in establishing long-range dependencies in CNN-based architectures, we design an encoder that combines a CNN module with a transformer module. This combination enables the network to focus on both local and global information. We evaluated our method and compared it to 11 competitive traditional and deep learning-based methods on the latest released multi-exposure image fusion benchmark dataset, and our method achieved the best performance in both subjective and objective evaluations. Code will be available at https: //github. com/miccaiif/TransMEF.

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