科研成果 by Type: 期刊论文

2025
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 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
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.
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
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
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
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
Shi R, Wang W, Li Z, He L, Sheng K, Ma L, Du K, Jiang T, Huang T. U-RISC: An Annotated Ultra-High-Resolution Electron Microscopy Dataset Challenging the Existing Deep Learning Algorithms. Frontiers in Computational Neuroscience [Internet]. 2022;16:842760. 访问链接
Liu Z, Li Z, Hu Z, Xia Q, Xiong R, Zhang S, Jiang T. Contrastive and Selective Hidden Embeddings for Medical Image Segmentation. IEEE Transations on Medical Imaging [Internet]. 2022;41:3398–3410. 访问链接
Liu Y, Jiang M, Jiang T. Transferable Adversarial Examples based on Global Smooth Perturbations. Computers & Security [Internet]. 2022;121:102816. 访问链接
2021
Roß T, Reinke A, Full PM, Wagner M, Kenngott H, Apitz M, Hempe H, M\^ındroc-Filimon D, Scholz P, Tran TN, et al. Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge. Medical Image Analysis [Internet]. 2021;70:101920. 访问链接
Li D, Jiang T, Jiang M. Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training. International Journal of Computer Vision [Internet]. 2021;129:1238–1257. 访问链接
2020
Liu D, Jiang T, Wang Y, Miao R, Shan F, Li Z. Clearness of Operating Field: a Surrogate for Surgical Skills on In Vivo Clinical Data. International Journal of Computer Assisted Radiology and Surgery [Internet]. 2020;15:1817–1824. 访问链接
2019
Li D, Jiang T, Jiang M. Recent Advances and Challenges in Video Quality Assessment. ZTE Communications [Internet]. 2019;17:3-11. 访问链接
Heng W, Jiang T, Gao W. How to Assess the Quality of Compressed Surveillance Videos Using Face Recognition. IEEE Transactions on Circuits and Systems for Video Technology [Internet]. 2019;29:2229–2243. 访问链接
Li D, Jiang T, Lin W, Jiang M. Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?. IEEE Transations on Multimedia [Internet]. 2019;21:1221–1234. 访问链接
2017
Fan Z, Jiang T, Huang T. Active Sampling Exploiting Reliable Informativeness for Subjective Image Quality Assessment Based on Pairwise Comparison. IEEE Transations on Multimedia [Internet]. 2017;19:2720–2735. 访问链接
2016
Huang C, Jiang M, Jiang T. Image Quality Assessment Based on Contour and Region. Journal of Computational Mathematics [Internet]. 2016;34:705–722. 访问链接Abstract
Image Quality Assessment (IQA) is a fundamental problem in image processing. It is a common principle that human vision is hierarchical: we first perceive global structural information such as contours then focus on local regional details if necessary. Following this principle, we propose a novel framework for IQA by quantifying the degenerations of structural information and region content separately, and mapping both to obtain the objective score. The structural information can be obtained as contours by contour detection techniques. Experiments are conducted to demonstrate its performance in comparison with multiple state-of-the-art methods on two large scale datasets.
Qi F, Zhao D, Fan X, Jiang T. Stereoscopic Video Quality Assessment Based on Visual Attention and Just-noticeable Difference Models. Signal, Image and Video Processing [Internet]. 2016;10:737–744. 访问链接

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