科研成果 by Type: Conference Paper

2025
Liu Z, Qiao L, Chu X, Ma L, Jiang T. Towards Efficient Foundation Model for Zero-shot Amodal Segmentation, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, June 11-15. Nashville, TN, USA: Computer Vision Foundation / IEEE; 2025:20254–20264. 访问链接
2024
Shi R, Pang Q, Ma L, Duan L, Huang T, Jiang T. ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation, in The 27th International Conference on Medical Image Computing and Computer Assisted Intervention,MICCAI 2024, October 6-10.Vol 15012. Marrakesh, Morocco: Springer; 2024:731–741. 访问链接
Ye W, Li Z, Jiang T. VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction, in The 17th Asian Conference on Computer Vision, ACCV 2024, December 8-12.Vol 15480. Hanoi, Vietnam: Springer; 2024:38–54. 访问链接
Liu C, Yu X, Wang D, Jiang T. ACLNet: A Deep Learning Model for ACL Rupture Classification Combined with Bone Morphology, in The 27th International Conference on Medical Image Computing and Computer Assisted Intervention,MICCAI 2024, October 6-10.Vol 15005. Marrakesh, Morocco: Springer; 2024:57–67. 访问链接Abstract
Magnetic Resonance Imaging (MRI) is widely used in diagnosing anterior cruciate ligament (ACL) injuries due to its ability to provide detailed image data. However, existing deep learning approaches often overlook additional factors beyond the image itself. In this study, we aim to bridge this gap by exploring the relationship between ACL rupture and the bone morphology of the femur and tibia. Leveraging extensive clinical experience, we acknowledge the significance of this morphological data, which is not readily observed manually. To effectively incorporate this vital information, we introduce ACLNet, a novel model that combines the convolutional representation of MRI images with the transformer representation of bone morphological point clouds. This integration significantly enhances ACL injury predictions by leveraging both imaging and geometric data. Our methodology demonstrated an enhancement in diagnostic precision on the in-house dataset compared to image-only methods, elevating the accuracy from 87.59% to 92.57%. This strategy of utilizing implicitly relevant information to enhance performance holds promise for a variety of medical-related tasks.
Liu Z, Li Z, Jiang T. BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion, in The 38th AAAI Conference on Artificial Intelligence, AAAI 2024, Feb. 20-27. Vancouver, Canada: AAAI Press; 2024:3846–3854. 访问链接
Zhong Y, Wu X, Zhang L, Yang C, Jiang T. Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference, in The 41st International Conference on Machine Learning, ICML 2024, July 21-27. Vienna, Austria; 2024. 访问链接
Liu Y, Yang C, Li D, Ding J, Jiang T. Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, June 16-22. Seattle, WA, USA: IEEE; 2024:25554–25563. 访问链接
Shi R, Duan L, Huang T, Jiang T. Evidential Uncertainty-Guided Mitochondria Segmentation for 3D EM Images, in The 38th AAAI Conference on Artificial Intelligence, AAAI 2024, Feb. 20-27. Vancouver, Canada: AAAI Press; 2024:4847–4855. 访问链接
2023
Li Z, Ye W, Terven JR, Bennett Z, Zheng Y, Jiang T, Huang T. MUVA: A New Large-Scale Benchmark for Multi-view Amodal Instance Segmentation in the Shopping Scenario, in IEEE/CVF International Conference on Computer Vision, ICCV 2023, October 1-6. Paris, France: IEEE; 2023:23447–23456. 访问链接
Li Z, Shi R, Huang T, Jiang T. OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation, in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, June 4-10. Rhodes Island, Greece: IEEE; 2023:1–5. 访问链接
Liu D, Li Q, Dinh A-D, Jiang T, Shah M, Xu C. Diffusion Action Segmentation, in IEEE/CVF International Conference on Computer Vision, ICCV 2023, October 1-6. Paris, France: IEEE; 2023:10105–10115. 访问链接
2022
Yi F, Yang Y, Jiang T. Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition, in The 16th Asian Conference on Computer Vision, ACCV 2022, December 4-8.Vol 13844. Macao, China: Springer; 2022:417–432. 访问链接
Li Z, Ye W, Jiang T, Huang T-J. 2D Amodal Instance Segmentation Guided by 3D Shape Prior, in The 17th European Conference on Computer Vision, ECCV 2022, October 23-27.Vol 13689. Tel Aviv, Israel: Springer; 2022:165–181. 访问链接
Liu Y, Jiang M, Jiang T. LabelFool: A Trick In The Label Space, in International Joint Conference on Neural Networks, IJCNN 2022, July 18-23. Padua, Italy: IEEE; 2022:1–8. 访问链接
Qin W, Xu R, Jiang S, Jiang T, Luo L. PathTR: Context-Aware Memory Transformer for Tumor Localization in Gigapixel Pathology Images, in Asian Conference on Computer Vision, ACCV 2022, December 4-8. Macao, China; 2022:3603-3619. 访问链接
2021
Yi F, Wen H, Jiang T. ASFormer: Transformer for Action Segmentation, in The 32nd British Machine Vision Conference, BMVC 2021, November 22-25. Online: BMVA Press; 2021:236. 访问链接
Liu Z, Xiong R, Jiang T. Multi-level Relationship Capture Network for Automated Skin Lesion Recognition, in The 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, September 27 - October 1.Vol 12907. Strasbourg, France: Springer; 2021:153–164. 访问链接
Li D, Jiang T, Jiang M, Thambawita VL, Wang H. Reproducibility Companion Paper: Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment, in ACM Multimedia Conference, October 20 - 24. Virtual Event: ACM; 2021:3615–3618. 访问链接
Liu D, Li Q, Jiang T, Wang Y, Miao R, Shan F, Li Z. Towards Unified Surgical Skill Assessment, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, June 19-25. Virtual: Computer Vision Foundation / IEEE; 2021:9522–9531. 访问链接
2020
Liu Z, Xiong R, Jiang T. Clinical-Inspired Network for Skin Lesion Recognition, in The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, October 4-8.Vol 12266. Lima, Peru: Springer; 2020:340–350. 访问链接

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