Haowei Guo

Haowei Guo

Posts by Collection

portfolio

publications

GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation

Published in ICME 2025 , 2025

“Multi-organ segmentation is a critical yet challenging task due to complex anatomical backgrounds, blurred boundaries, and diverse morphologies. This study introduces the Gradient-aware Adaptive Momentum Evolution Deep Snake (GAMED-Snake) model, which establishes a novel paradigm for contour-based segmentation by integrating gradient-based learning with adaptive momentum evolution mechanisms. The GAMED-Snake model incorporates three major innovations: First, the Distance Energy Map Prior (DEMP) generates a pixel-level force field that effectively attracts contour points towards the true boundaries, even in scenarios with complex backgrounds and blurred edges. Second, the Differential Convolution Inception Module (DCIM) precisely extracts comprehensive energy gradients, significantly enhancing segmentation accuracy. Third, the Adaptive Momentum Evolution Mechanism (AMEM) employs cross-attention to establish dynamic features across different iterations of evolution, enabling precise boundary alignment for diverse morphologies. Experimental results on four challenging multi-organ segmentation datasets demonstrate that GAMED-Snake improves the mDice metric by approximately 2% compared to state-of-the-art methods. Code will be available at https://github.com/SYSUzrc/GAMED-Snake.“

Recommended citation: Zhang R, Guo H, Zhang Z, et al. Gamed-snake: Gradient-aware adaptive momentum evolution deep snake model for multi-organ segmentation[J]. arXiv preprint arXiv:2501.12844, 2025.
Download Paper

Unified Medical Image Segmentation with State Space Modeling Snake

Published in ACM MM 2025 , 2025

Unified Medical Image Segmentation (UMIS) is critical for comprehensive anatomical assessment but faces challenges due to multi-scale structural heterogeneity. Conventional pixel-based approaches, lacking object-level anatomical insight and inter-organ relational modeling, struggle with morphological complexity and feature conflicts, limiting their efficacy in UMIS. We propose Mamba Snake, a novel deep snake framework enhanced by state space modeling for UMIS. Mamba Snake frames multi-contour evolution as a hierarchical state space atlas, effectively modeling macroscopic inter-organ topological relationships and microscopic contour refinements. We introduce a snake-specific vision state space module, the Mamba Evolution Block (MEB), which leverages effective spatiotemporal information aggregation for adaptive refinement of complex morphologies. Energy map shape priors further ensure robust long-range contour evolution in heterogeneous data. Additionally, a dual-classification synergy mechanism is incorporated to concurrently optimize detection and segmentation, mitigating under-segmentation of microstructures in UMIS. Extensive evaluations across five clinical datasets reveal Mamba Snake’s superior performance, with an average Dice improvement of 3% over state-of-the-art methods.

Recommended citation: Zhang R, Guo H, Tian K, et al. Unified Medical Image Segmentation with State Space Modeling Snake[J]. arXiv preprint arXiv:2507.12760, 2025.
Download Paper

LongCat-Next: Lexicalizing Modalities as Discrete Tokens

Published in , 2026

“We develop LongCat-Next, a native multimodal model that processes text, vision, and audio under a single autoregressive objective with minimal inductive bias beyond the language paradigm. As an industrial-strength foundation model with A3B model size, it excels at seeing, creating, and talking, achieving strong performance across a wide range of multimodal benchmarks. In particular, leveraging semantically complete discrete representations, it surpasses the long-standing performance ceiling of discrete vision modeling on understanding tasks, and provides a unified solution for visual understanding and generation. This success demonstrates that discrete tokens can universally represent multimodal signals and be deeply internalized within a single discrete embedding space. We further provide extensive experiments to analyze this unified discrete training paradigm and uncover several interesting findings.As a meaningful attempt toward native multimodality, we open-source the LongCat-Next and its tokenizers, hoping to foster further research and development in the community.“

Recommended citation: Meituan LongCat Team, "LongCat-Next: Lexicalizing Modalities as Discrete Tokens," arXiv preprint arXiv:2603.27538, 2026.
Download Paper

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.