Publications

Works in Progress

• Se-In Jang, Cristina Lois, John Alex Becker, Emma Thibault, Julie C. Price, Keith A. Johnson, Georges El Fakhri, and Kuang Gong, “A Cross-modality Transformer Network for Low-dose Tau PET/MR Imaging”, TBD, 2023.

• Se-In Jang, “Deterministic Online Classification: Non-iteratively Reweighted Recursive Least-Squares for Binary Class Rebalancing”, 2023.

• Se-In Jang, “Online Passive-Aggressive Total-Error-Rate Minimization”, arXiv Preprint, 2020.

Journals

• Se-In Jang, Tinsu Pan, Ye Li, Pedram Heidari, Junyu Chen, Quanzheng Li, and Kuang Gong, “Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising”, IEEE Transactions on Medical Imaging (IF = 10.6), Nov 2023. [Code]

• Zainab Alhakeem, Se-In Jang, and Hong-Goo Kang, “Disentangled Representations in Local-Global Contexts for Arabic Dialect Identification”, IEEE/ACM Transactions on Audio, Speech, and Language Processing (IF = 5.4), Nov 2023.

• Gary Y. Li, Junyu Chen, Se-In Jang, Kuang Gong, and Quanzheng Li, “SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images”, Medical Physics (IF = 3.8), Sep 2023.

• Se-In Jang, Geok-Choo Tan, Kar-Ann Toh and Andrew Beng Jin Teoh, “Online Heterogeneous Face Recognition Based on Total-Error-Rate Minimization”, IEEE Trans. on Systems, Man, and Cybernetics: Systems (IF = 13.451), vol. 50, no. 4, pp. 126-139, Apr 2020.

• Se-In Jang, Kwontaeg Choi, Kar-Ann Toh, Andrew Beng Jin Teoh and Jaihie Kim, “Object Tracking Based on An Online Learning Network with Total Error Rate Minimization”, Pattern Recognition (IF = 7.74), vol. 48, no. 1, pp. 126-139, Jan 2015.

Conferences

• Se-In Jang, Cristina Lois, Emma Thibault, J. Alex Becker, Yafei Dong, Marc D. Normandin, Julie C. Price, Keith A. Johnson, Georges El Fakhri, and Kuang Gong, “TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models”, IEEE Medical Imaging Conference (MIC), 2023.

• Se-In Jang, Tinsu Pan, and Kuang Gong, “Phase-matched PET/CT Imaging through Data-driven Gated CT and Transformer network-based Image Registration”, SNMMI, 2023.

• Se-In Jang, Cristina Lois, John Alex Becker, Emma Thibault, Julie C. Price, Keith A. Johnson, Georges El Fakhri, and Kuang Gong, “Cross-modality Transformer for Low-dose Tau PET Imaging”, Human Amyloid Imaging Conference (HAI), 2023.

• Se-In Jang, Cristina Lois, John Alex Becker, Emma Thibault, Ye Li, Julie C. Price, Georges El Fakhri, Quanzheng Li, Keith A. Johnson, and Kuang Gong, “Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention”, IEEE Medical Imaging Conference (MIC), 2022.

• Se-In Jang, Tinsu Pan, Ye Li, Junyu Chen, Quanzheng Li, and Kuang Gong, “PET image denoising based on transformer: evaluations on datasets of multiple tracers”, The Society of Nuclear Medicine and Molecular Imaging (SNMMI), 2022.

• Se-In Jang, Michaël J.A. Girard and Alexandre Thiery, “Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning”, The International Workshop on Trustworthy AI for Healthcare (TAIH) at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI22), 2022.

• Se-In Jang, Michaël J.A. Girard and Alexandre Thiery, “Explainable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning”, The 15th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy) at International Joint Conference on Learning & Reasoning, 2021.

• Se-In Jang, Geok-Choo Tan and Kar-Ann Toh, “Recursive Total Error Rate Minimization”, IEEE Region 10 Conference (TENCON), 2016.

• Se-In Jang, Kangrok Oh, Andrew Beng Jin Teoh and Kar-Ann Toh, “Object Tracking Based on Kernel Recursive Least-Squares with Total Error Rate Minimization”, The IEEE Conference on Industrial Electronics and Applications (ICIEA), 2013.

• Se-In Jang, Kwontaeg Choi, Youngsung Kim, Beom-Seok Oh and Kar-Ann Toh, “Visual Tracking with Online Discriminative Learning”, The International Conference on Information, Communications and Signal Processing (ICICS), 2011.

Book

• Se-In Jang and Kuang Gong, “Attenuation Correction for Quantitative PET/MR Imaging”, Chapter 8, Medical Image Synthesis: Methods and Clinical Applications, Jul 2023.

Talks

• A Cross-modality Transformer Network for Low-dose Tau PET/CT Imaging, Harvard Medical School, MA, United States, Mar 2023.

• Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising, Harvard Medical School, MA, United States, Sep 2022.

• Self-supervised Learning and Neural-symbolic Learning for Medical Image Analysis, Harvard Medical School, MA, United States, Jul 2021.

• Self-supervised Learning and Neural-symbolic Learning for Retinal Image Analysis, University of California, San Francisco (UCSF), CA, United States, Mar 2021.

• Online Learning for Classification, National University of Singapore (NUS), Singapore, Sep 2019.

• Online Learning Based on Total-error-rate Minimization, KEPCO KDN, South Korea, Apr 2017.