arXiv subjects

Guohao Huo

Publications and source records attributed to Guohao Huo.

5 recordsLinked to original sources

arXiv2025

Graph-Based Multi-Modal Light-weight Network for Adaptive Brain Tumor Segmentation

Multi-modal brain tumor segmentation remains challenging for practical deployment due to the high computational costs of mainstream models. In this work, we propose GMLN-BTS, a Graph-based Multi-modal interaction Lightweight Network for brain tumor segmentation. Our architecture achieves high-precision, resource-efficient segmentation through three key components. First, a Modality-Aware Adaptive Encoder (M2AE) facilitates efficient multi-scale semantic extraction. Second, a Graph-based Multi-Modal Collaborative Interaction Module (G2MCIM) leverages graph structures to model complementary cross-modal relationships. Finally, a Voxel Refinement UpSampling Module (VRUM) integrates linear interpolation with multi-scale transposed convolutions to suppress artifacts and preserve boundary details. Experimental results on BraTS 2017, 2019, and 2021 benchmarks demonstrate that GMLN-BTS achieves state-of-the-art performance among lightweight models. With only 4.58M parameters, our method reduces parameter count by 98% compared to mainstream 3D Transformers while significantly outperforming existing compact approaches.

eess.IV↗

arXiv2025

SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception

To address complex pathological feature extraction in automated cardiac MRI segmentation, we propose SAMba-UNet, a novel dual-encoder architecture that synergistically combines the vision foundation model SAM2, the linear-complexity state-space model Mamba, and the classical UNet to achieve cross-modal collaborative feature learning; to overcome domain shifts between natural images and medical scans, we introduce a Dynamic Feature Fusion Refiner that employs multi-scale pooling and channel-spatial dual-path calibration to strengthen small-lesion and fine-structure representation, and we design a Heterogeneous Omni-Attention Convergence Module (HOACM) that fuses SAM2's local positional semantics with Mamba's long-range dependency modeling via global contextual attention and branch-selective emphasis, yielding substantial gains in both global consistency and boundary precision-on the ACDC cardiac MRI benchmark, SAMba-UNet attains a Dice of 0.9103 and HD95 of 1.0859 mm, notably improving boundary localization for challenging structures like the right ventricle, and its robust, high-fidelity segmentation maps are directly applicable as a perception module within intelligent medical and surgical robotic systems to support preoperative planning, intraoperative navigation, and postoperative complication screening; the code will be open-sourced to facilitate clinical translation and further validation.

cs.CV↗

arXiv2025

DMS-Net:Dual-Modal Multi-Scale Siamese Network for Binocular Fundus Image Classification

Ophthalmic diseases pose a significant global health burden. However, traditional diagnostic methods and existing monocular image-based deep learning approaches often overlook the pathological correlations between the two eyes. In practical medical robotic diagnostic scenarios, paired retinal images (binocular fundus images) are frequently required as diagnostic evidence. To address this, we propose DMS-Net-a dual-modal multi-scale siamese network for binocular retinal image classification. The framework employs a weight-sharing siamese ResNet-152 architecture to concurrently extract deep semantic features from bilateral fundus images. To tackle challenges like indistinct lesion boundaries and diffuse pathological distributions, we introduce the OmniPool Spatial Integrator Module (OSIM), which achieves multi-resolution feature aggregation through multi-scale adaptive pooling and spatial attention mechanisms. Furthermore, the Calibrated Analogous Semantic Fusion Module (CASFM) leverages spatial-semantic recalibration and bidirectional attention mechanisms to enhance cross-modal interaction, aggregating modality-agnostic representations of fundus structures. To fully exploit the differential semantic information of lesions present in bilateral fundus features, we introduce the Cross-Modal Contrastive Alignment Module (CCAM). Additionally, to enhance the aggregation of lesion-correlated semantic information, we introduce the Cross-Modal Integrative Alignment Module (CIAM). Evaluation on the ODIR-5K dataset demonstrates that DMS-Net achieves state-of-the-art performance with an accuracy of 82.9%, recall of 84.5%, and a Cohen's kappa coefficient of 83.2%, showcasing robust capacity in detecting symmetrical pathologies and improving clinical decision-making for ocular diseases. Code and the processed dataset will be released subsequently.

cs.CV↗

arXiv2025

Frequency Domain Enhanced U-Net for Low-Frequency Information-Rich Image Segmentation in Surgical and Deep-Sea Exploration Robots

In deep-sea exploration and surgical robotics scenarios, environmental lighting and device resolution limitations often cause high-frequency feature attenuation. Addressing the differences in frequency band sensitivity between CNNs and the human visual system (mid-frequency sensitivity with low-frequency sensitivity surpassing high-frequency), we experimentally quantified the CNN contrast sensitivity function and proposed a wavelet adaptive spectrum fusion (WASF) method inspired by biological vision mechanisms to balance cross-frequency image features. Furthermore, we designed a perception frequency block (PFB) that integrates WASF to enhance frequency-domain feature extraction. Based on this, we developed the FE-UNet model, which employs a SAM2 backbone network and incorporates fine-tuned Hiera-Large modules to ensure segmentation accuracy while improving generalization capability. Experiments demonstrate that FE-UNet achieves state-of-the-art performance in cross-domain tasks such as marine organism segmentation and polyp segmentation, showcasing robust adaptability and significant application potential. The code will be released soon.

cs.CV↗

arXiv2024

G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning

Commonsense question answering has demonstrated considerable potential across various applications like assistants and social robots. Although fully fine-tuned pre-trained Language Models(LM) have achieved remarkable performance in commonsense reasoning, their tendency to excessively prioritize textual information hampers the precise transfer of structural knowledge and undermines interpretability. Some studies have explored combining LMs with Knowledge Graphs(KGs) by coarsely fusing the two modalities to perform Graph Neural Network(GNN)-based reasoning that lacks a profound interaction between heterogeneous modalities. In this paper, we propose a novel Graph-based Structure-Aware Prompt Learning Model for commonsense reasoning, named G-SAP, aiming to maintain a balance between heterogeneous knowledge and enhance the cross-modal interaction within the LM+GNNs model. In particular, an evidence graph is constructed by integrating multiple knowledge sources, i.e. ConceptNet, Wikipedia, and Cambridge Dictionary to boost the performance. Afterward, a structure-aware frozen PLM is employed to fully incorporate the structured and textual information from the evidence graph, where the generation of prompts is driven by graph entities and relations. Finally, a heterogeneous message-passing reasoning module is used to facilitate deep interaction of knowledge between the LM and graph-based networks. Empirical validation, conducted through extensive experiments on three benchmark datasets, demonstrates the notable performance of the proposed model. The results reveal a significant advancement over the existing models, especially, with 6.12% improvement over the SoTA LM+GNNs model on the OpenbookQA dataset.

cs.CL↗