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 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 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AbstractMagnetic 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 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