Selected work

Representative Papers

3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment

Aug 11, 2026

Traditional 2D approaches struggle to model the three-dimensional anatomical structure of sheep faces and the spatial relationships among facial keypoints, limiting the accuracy of Sheep Pain Facial Expression Scale (SPFES)-based pain assessment. This work proposes 3D-SPFES, a novel system that integrates monocular RGB-based depth estimation with a weighted geometric graph neural network (WG-GNN). Leveraging VideoDepthAnything to recover depth, the method embeds facial keypoints into 3D Euclidean space and constructs edge weights that combine Euclidean distances with surface coplanarity. An anatomy-aware scaled dot-product attention mechanism is introduced to facilitate geometric message passing, ultimately yielding a continuous, normalized pain score ranging from 0% to 100%. Requiring no specialized depth-sensing hardware, this approach substantially enhances the modeling of SPFES-relevant features and their 3D spatial dependencies, enabling highly accurate automated pain assessment.

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Recent Advances in Medical Imaging Segmentation: A Survey

May 14, 2025

Medical image segmentation faces fundamental challenges including data scarcity, high annotation costs, poor cross-modal and cross-domain generalizability, and stringent privacy constraints. To address these, this work systematically reviews over 200 state-of-the-art publications and—uniquely—integrates perspectives from generative AI (e.g., diffusion models) and foundation models into a clinically oriented evaluation framework. We delineate adaptation pathways for foundation models in medical segmentation, identify critical bottlenecks (e.g., domain misalignment, computational overhead), and propose lightweight fine-tuning strategies—including visual prompting, multimodal fusion, and self-supervised pretraining. Our contributions include a continuously updated, open-source knowledge repository on GitHub, featuring a structured technical roadmap that bridges algorithmic innovation with clinical translation. This resource supports both methodological development and real-world deployment of segmentation models in healthcare settings.

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Recent publications

Latest Papers

3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment

Aug 11, 2026

Traditional 2D approaches struggle to model the three-dimensional anatomical structure of sheep faces and the spatial relationships among facial keypoints, limiting the accuracy of Sheep Pain Facial Expression Scale (SPFES)-based pain assessment. This work proposes 3D-SPFES, a novel system that integrates monocular RGB-based depth estimation with a weighted geometric graph neural network (WG-GNN). Leveraging VideoDepthAnything to recover depth, the method embeds facial keypoints into 3D Euclidean space and constructs edge weights that combine Euclidean distances with surface coplanarity. An anatomy-aware scaled dot-product attention mechanism is introduced to facilitate geometric message passing, ultimately yielding a continuous, normalized pain score ranging from 0% to 100%. Requiring no specialized depth-sensing hardware, this approach substantially enhances the modeling of SPFES-relevant features and their 3D spatial dependencies, enabling highly accurate automated pain assessment.

0 citationsRead paper

Recent Advances in Medical Imaging Segmentation: A Survey

May 14, 2025

Medical image segmentation faces fundamental challenges including data scarcity, high annotation costs, poor cross-modal and cross-domain generalizability, and stringent privacy constraints. To address these, this work systematically reviews over 200 state-of-the-art publications and—uniquely—integrates perspectives from generative AI (e.g., diffusion models) and foundation models into a clinically oriented evaluation framework. We delineate adaptation pathways for foundation models in medical segmentation, identify critical bottlenecks (e.g., domain misalignment, computational overhead), and propose lightweight fine-tuning strategies—including visual prompting, multimodal fusion, and self-supervised pretraining. Our contributions include a continuously updated, open-source knowledge repository on GitHub, featuring a structured technical roadmap that bridges algorithmic innovation with clinical translation. This resource supports both methodological development and real-world deployment of segmentation models in healthcare settings.

0 citationsRead paper