Peng Luo

JBHI Journal 2025 Journal Article

Edge-Guided Multi-Scale Frequency Attention Network for Gastrointestinal Cancer Image Segmentation

• Zhiwen Liao
• Qi Wang
• Xinyi Tang
• Han Wang
• Jun Hu
• Pengxiang Su
• Evangelos K. Markakis
• Peng Luo

Image segmentation is a critical technology to improve the accuracy of clinical decisions and treatments in computer-aided diagnostic systems. However, the diverse morphology and fuzzy boundaries of gastrointestinal tumors incur substantial challenges for existing segmentation models, leading to inaccurate feature capture and generating suboptimal results. For solving these problems, we design an edge-guided multi-scale frequency attention network for the gastrointestinal tumor segmentation task, termed EGMFA-Net, which consists of a Kernel Adaptive Enhancement Module (KAEM) and a Frequency-domain Self-attention Module (FDSA). Specifically, KAEM adaptively adjusts the feature extraction kernel based on the morphology of different lesion regions, which enhances the recognition of different morphology regions via a progressive optimization strategy of feature expression. Furthermore, FDSA effectively aggregates multi-scale features in the frequency domain to achieve global receptive fields while preserving more high-frequency details, thereby enhancing adaptability to complex pathological contexts. Extensive experiments on eight medical image benchmark datasets, including SEED, Kvasir, ClinicDB, ColonDB, ETIS, BKAI, CVC-300, and Synapse, show that EGMFA-Net attains state-of-the-art performance over existing methods. Our implementation is available at https://github.com/med-segment/egmfa-net.

AAAI Conference 2023 Conference Paper

Real-World Deep Local Motion Deblurring

• Haoying Li
• Ziran Zhang
• Tingting Jiang
• Peng Luo
• Huajun Feng
• Zhihai Xu

Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. Extensive experiments prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves better performance than state-of-the-art global deblurring methods and our proposed local blur-aware techniques are effective.

YNICL Journal 2019 Journal Article

Comparison of electrical impedance tomography and intracranial pressure during dehydration treatment of cerebral edema

• Bin Yang
• Bing Li
• Canhua Xu
• Shijie Hu
• Meng Dai
• Junying Xia
• Peng Luo
• Xuetao Shi

TIST Journal 2015 Journal Article

Bounds on Direct and Indirect Effects of Treatment on a Continuous Endpoint

• Peng Luo
• Zhi Geng

Direct effect of a treatment variable on an endpoint variable and indirect effect through a mediate variable are important concepts for understanding a causal mechanism. However, the randomized assignment of treatment is not sufficient for identifying the direct and indirect effects, and extra assumptions and conditions are required, such as the sequential ignorability assumption without unobserved confounders or the sequential potential ignorability assumption. But these assumptions may not be credible in many applications. In this article, we consider the bounds on controlled direct effect, natural direct effect, and natural indirect effect without these extra assumptions. Cai et al. [2008] presented the bounds for the case of a binary endpoint, and we extend their results to the general case for an arbitrary endpoint.