科研成果

2025
Wang H, Liu Z, Pan H, Liu K, Wen Y, Qin Y, Dang J, Li M, Cui Z, Jiang T, et al. A 3-Dimensional-Optimized Artificial Imaging Model for the Skin Tumor Burden Assessment of Mycosis Fungoides. Journal of Investigative Dermatology [Internet]. 2025. 访问链接Abstract
Mycosis fungoides is characterized by widespread skin patches that may progress to plaques and tumors, necessitating precise tumor burden assessment for staging and treatment guidance. However, existing methods, including the widely accepted modified Severity Weighted Assessment Tool (mSWAT), present significant challenges in routine practice owing to their time-consuming nature and interobserver variability. This study developed an artificial intelligence model, mSWAT-Net, to estimate mSWAT scores using clinical images of patients with mycosis fungoides. Notably, the overlap area segmentation submodule of mSWAT-Net addressed double-counting errors in multiangle photos through training on 3904 annotated images generated from 61 three-dimensional human images. Across 2463 standardized full-body photographs from 134 imaging series, mSWAT-Net demonstrated performance comparable with that of experienced cutaneous lymphoma specialists, achieving intraclass correlation coefficients of 0.917 (internal validation) and 0.846 (temporal validation) for mSWAT score. Moreover, mSWAT-Net outperformed 3 junior dermatologists in image-based scoring (intraclass correlation coefficient = 0.917 vs 0.777) and demonstrated robust performance when compared with ground truth derived from 3-dimensional patient imaging (intraclass correlation coefficient = 0.812). Finally, mSWAT-Net was deployed as a free web application to support mycosis fungoides management in clinical settings. These findings highlight the potential of mSWAT-Net as an accurate, automated clinical tool for facilitating patient follow-up, treatment monitoring, and remote consultations.
Shi R, Liu Z, Duan L, Jiang T. Amodal Segmentation for Laparoscopic Surgery Video Instruments. Sensing and Imaging [Internet]. 2025;26. 访问链接
Liu D, Li Q, Dinh A-D, Jiang T, Shah M, Xu C. DiffAct++: Diffusion Action Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence [Internet]. 2025;47:1644–1659. 访问链接
Liu Y, Yang C, Li D, Jiang T, Huang T. A Norm Regularization Training Strategy for Robust Image Quality Assessment Models. International Journal of Computer Vision [Internet]. 2025:1-15. 访问链接
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
Yang C, Liu Y, Li D, Jiang T. Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method. IEEE Transactions on Circuits and Systems for Video Technology [Internet]. 2024;34:12715–12729. 访问链接
Li Z, Ye W, Jiang T, Huang T. GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation. IEEE Transations on Multimedia [Internet]. 2024;26:3924–3936. 访问链接
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
Liu Z, Xiong R, Jiang T. CI-Net: Clinical-Inspired Network for Automated Skin Lesion Recognition. IEEE Transations on Medical Imaging [Internet]. 2023;42:619–632. 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
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
Shi R, Bi K, Du K, Ma L, Fang F, Duan L, Jiang T, Huang T-J. PS-Net: Human Perception-guided Segmentation Network for EM Cell Membrane. Bioinformatics [Internet]. 2023;39:btad464. 访问链接
2022
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

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