Vision Systems and Intelligence

Journal Publications

[J1] Noelle Daigle, Shuyuan Guan, Suzann Duan, Thomas G. Knapp, [et. al., including Eungjoo Lee], "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms, 2(4), 045001 (2025).

[J2] Eungjoo Lee, Lei Pan, Zhu Li, Shuvra S Bhattacharyya, "Efficient Hyperspectral Image Classification using Discrete Cosine Transform on Limited-Resource Systems," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2025).
[J3] Eungjoo Lee, Meghan L Edgerton, Barbara Buccilli, Ilknur Telkes, Tessa Harland, Julie G Pilitsis, "Prediction of response to spinal cord stimulation using machine learning based on radiomics and patient reported outcomes," Neurosurgery (2025).
[J4] Mohammadreza Saraei, Mehrshad Lalinia, Eung-Joo Lee, "Deep Learning-Based Medical Object Detection: A Survey," IEEE Access (2025).
[J5] Reza Monfaredi, Pavel Yarmolenko, Eung-Joo Lee, Kevin Cleary, and Karun Sharma, "Evaluation of a novel MRI-safe needle guidance toolkit for streamlined arthrography procedures." Scientific Reports 13.1 (2023): 17610.

[J6] Vaishanth Ramaraj, Atharva Parlikar, Eung-Joo Lee, S Anwar, and Reza Monfaredi, “Development of a real-time autonomous modular system for a powered wheelchair,” Journal of Signal Processing Systems (2023): 1-12.

[J7] Martin Wagner, Beat-Peter Muller-Stich, [et. al., including Eung-Joo Lee], “Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark,” Medical Image Analysis (2023): 102770.

[J8] Sung Jin Lee, Jun Seok Yun, Eung-Joo Lee, Seokbong Yoo, “HIFA-LPR: High-Frequency Augmented License Plate Recognition in Low-Quality Legacy Conditions via Gradual End-to-End Learning,” Mathematics 10.9 (2022): 1569.

[J9] Taeyoon Kim, Yongju Kwon, Jooyong Lee, Eung-Joo Lee, Soonchul Kwon, ”Wave attenuation prediction of an artificial coral reef using machine-learning integrated with hydraulic experiment,” Ocean Engineering, Mar. 2022.

[J10] Eung-Joo Lee, Setareh Farzinfard, Pavel S. Yarmolenko, Kevin Cleary, and Reza Monfaredi, “Toward robust partial-image based template matching techniques for MRI-guided interventions,” Journal of Digital Imaging, Oct. 2022.

[J11] Eung-Joo Lee, William Plishker, Nobuhiko Hata, Paul B. Shyn, Stuart G. Silverman, Shuvra S. Bhattacharyya, Raj Shekhar, “Rapid Quality Assessment of Nonrigid Image Registration Based on Supervised Learning,” Journal of Digital Imaging 34.6 (2021): 1376-1386.

[J12] Seok Bong Yoo, Eung-Joo Lee, “Deep super-resolution imaging technology: toward optical super-vision,” IEEE Consumer Electronics Magazine 10.1 (2020): 24-31.

[J13] Yaesop Lee, Jing Xie, Eung-Joo Lee, Srijesh Sudarsanan, Da-ting Lin, Rong Chen, Shuvra S. Bhattacharyya, “Real-time neuron detection and neural signal extraction platform for miniature calcium imaging,” Frontiers in computational neuroscience 14 (2020): 43.

[J14] Eung-Joo Lee, William Plishker, Xinyang Liu, Shuvra S. Bhattacharyya, Raj Shekhar, “Weakly Supervised Segmentation for Real-time Surgical Tool Tracking,” IET Healthcare Technology Letters: 231-2361, Oct. 2019.

Conference and Workshop Publications

[C1] Yicheng Leng, Syed Muhammad Anwar, Islem Rekik, Sen He, Eung-Joo Lee, "Self-Supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis Towards Improving Autism Detection," IEEE International Symposium on Biomedical Imaging, 2025
[C2] Jiayi Zhang, Yuchen Tian, Bofan Song, Eung-Joo Lee, "Multimodal Learning for Enhanced Detection in Oral Cancer Screening," IEEE International Symposium on Biomedical Imaging, 2025
[C3] Aravind Shrenivas Murali, Marino Chuquilín, Eung-Joo Lee, "Multi-scale Attention Network for Diabetic Foot Ulcer Segmentation Using Self-supervised Learning." Diabetic Foot Ulcers Grand Challenge. Cham: Springer Nature Switzerland, 2024. 81-92.

[C4] Yicheng Leng, Eung-Joo Lee, “SLRBC: Self-supervised Learning for Red Blood Cell Segmentation and Classification with a Biased Contrastive Loss," SPIE Medical Imaging 2025
[C5] Yuchen Tian, Sidike Paheding, Ehsan Azimi, Eung-Joo Lee, "Exploring Action Recognition in Endoscopy Video Datasets," Real-Time Image Processing and Deep Learning 2024. SPIE, 2024

[C6] Sidike Paheding, Nusrat Zahana, Abel A. Reyes, Ernesto Martinez Jr, Eung-Joo Lee, "Hyperspectral Image Classification with Retentive Network," Pattern Recognition and Tracking XXXV 2024. SPIE, 2024.

[C7] Eungi Lee, Eung-Joo Lee, Syed Muhammad Anwar, Seok Bong Yoo, “Child FER: Domain-Agnostic Facial Expression Recognition in Children using a Secondary Image Diffusion Model,” IEEE International Conference on Acoustics, Speech and Signal Processing, 2024

[C8] Yujunrong Ma, Kiminori Nakamura, Eung-Joo Lee, Shuvra S. Bhattacharyya, “EADTC: An Approach to Interpretable and Accurate Crime Prediction,” 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022.

[C9] Eung-Joo Lee, Alex Mattingly, Heesung Kwon, Benjamin Riggan, Shuvra S. Bhattacharyya, “Real-time Face Tracking Based on a Configurable Ensemble of Detectors,” IEEE Asilomar Conference on Signals, Systems, and Computers, Oct. 2022.

[C10] Eung-Joo Lee, Alex Mattingly, Jing Xie, Shuvra S. Bhattacharyya, “Object detection on resource-constrained platforms using a configurable ensemble of detectors,” Real-Time Image Processing and Deep Learning 2022. Vol. 12102. SPIE, 2022.

[C11] Yaesop Lee, Eung-Joo Lee, Shuvra S. Bhattacharyya, Damon M. Conover, Heesung Kwon, Jeremy Leong, Jason Hill, Kenneth Evensen, “Archangel Dataset: UAV-based Imagery with Position and Pose Metadata,” Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV. Vol. 12113. SPIE, 2022.

[C12] Jieun Kim, Eung-Joo Lee, Deokwoo Lee, “Recognition of Facial Expression using Spatial Transformation Network and Convolutional Neural Network,” Pattern Recognition and Tracking XXXIII. Vol. 12101. SPIE, 2022.

[C13] Joohyung Lee, Eung-Joo Lee (Co-first author), “Self-supervised pre-training improves fundus image classification for diabetic retinopathy,” Real-Time Image Processing and Deep Learning 2022. Vol. 12102. SPIE, 2022.

[C14] Eung-Joo Lee, Yi-Ting Shin, Lei Pan, Zhu Li, Shuvra S. Bhattacharyya, “DCT-based Hyperspectral Image Classification on Resource-Constrained Platforms,” 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2021.

[C15] Eung-Joo Lee, Damon Conover, Heesung Kwon, Shuvra S. Bhattacharyya, “Validation of object detection in UAV-based images using synthetic data,” Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III. Vol. 11746. SPIE, 2021.

[C16] Reza Monfaredi, Pavel Yarmolenko, Eung-Joo Lee, Viktoriya Beskin, Kevin Cleary, Karun Sharma, “MRI-compatible needle guidance toolkit that streamlines arthrography procedures: phantom accuracy study,” Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling. Vol. 11315. SPIE, 2020. Feb. 2020.

[C17] Honglei Li, Lei Pan, Eung-Joo Lee, Zhu Li, Matthew J. Hoffmanz, Anthony Vodacekz, Shuvra S. Bhattacharyya, “Hyperspectral Video Processing on Resource-Constrained Platforms,” 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, Oct. 2019.

[C18] Eung-Joo Lee, William Plishker, Xinyang Liu, Timothy Kane, Shuvra S. Bhattacharyya, and Raj Shekhar, “Segmentation of surgical instruments in laparoscopic videos: training dataset generation and deep-learning-based framework,” Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511T, Mar 2019.

Abstracts and Extended Abstracts

[A1] Eshaan Mathakari, Kashyap Nadendla, Tanya Evita George, Zenith Mesa, Eungjoo Lee, "Antelligence: LLM-Powered Swarm Simulation with Biomedical Extensions," IEEE EMBS-BSN, 2025
[A2] Yong Jun Kim, DongKyun Kim, [et al., including Eung-Joo Lee], "Automated Segmentation of Cell Nuclei in Scattering-based Light Sheet Microscopy Images," Optica Biophotonics, 2024
[A3] Eung-Joo Lee, Seok Bong Yoo, “SREndo: Leveraging super-resolution techniques on endoscopic datasets for enhanced surgical procedures," Arizona Photonics Days, Jan. 2024.

[A4] Eckhoff Jennifer, Qeunzheng Li, [et al., including Eung-Joo Lee], “Best practices for Surgical Data Challenges tailored to Artificial Intelligence,” EAES 2023 Annual Meeting, June 2023.

[A5] A. Ali*, S. Hegde*, T. T. Pierce, E. Cheah, P. Guo, E. Lee, F. Heidari, A. Ozturk, K. Pope, A. R. Shih, J. Misdraji, A. E. Samir. 2023. AI-SCD: A deep learning approach for skin-to-liver capsule distance measurement to optimize shear wave elastography performance. Radiological Society of North America (RSNA) 2023.

Workshop Publications

[W1] Eung-Joo Lee, Vivek K. Singh, Elham Y. Kalafi, Peng Guo, Arinc Ozturk, Theodore T. Pierce, Brian A. Telfer, Anthony E. Samir & Laura J. Brattain, "Abdominal Ultrasound Similarity Analysis for Quantitative Longitudinal Liver Fibrosis Staging," Workshop on Clinical Image-Based Procedures. Cham: Springer Nature Switzerland, 2024
[W2] Yaesop Lee, Hyungtae Lee, Eung-Joo Lee, Heesung Kwon, Shuvra S. Bhattacharyya, “Exploiting Simplified Depth Estimation for Stereo-based 2D Object Detection,” IEEE Applied Imagery Pattern Recognition Workshop, 2022.

[W3] Eung-Joo Lee, Damon Conover, Hyungtae Lee, Heesung Kwon, “Validation of object detection in UAV-based images using synthetic data,” Automatic Target Recognizer Working Group 76th, Jan. 2022.

[W4] Eung-Joo Lee, Young-woo Jeon, Chan-hum Park, Sang-hyun Hwang, Jong-beom Ra, “Registration between camera image and digital elevation model,” 27th Workshop on Image Processing and Image Understanding (IPIU), Dec. 2015.

[W5] Eung-Joo Lee, Young-woo Jeon, Yong-jin Lee, and Sang-hyun Hwang, Jong-beom Ra, “Automatic registration of the digital elevation model to an aerial image,” Workshop on Image Processing and Image Understanding (IPIU) Summer Conference, June 2015.

Technical Reports

[T1] Yujunrong Ma, Kiminori Nakamura, Eung-Joo Lee, Shuvra S Bhattacharyya, “National Institute of Justice’s Recidivism Forecasting Challenge: Research Paper, Group MNLB,” National Institute of Justice, Aug 2022.

[T2] Jie Yan, Eung-Joo Lee, Damon Conover, Heesung Kwon, “Synthetic Dataset Generation and Adaptation for Human Detection,” Technical Report ARL-TR-9112, US Army Research Laboratory, Aug. 2020.

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