Tae Keun Yoo - RankMe

26 papers Journal 19Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Correction to: Deep learning prediction of steep and flat corneal curvature using fundus photography in post‑COVID telemedicine era. Medical Biol. Eng. Comput. Joon Yul Choi, Hyungsu Kim, Jin Kuk Kim, In Sik Lee, Ik Hee Ryu, Jung Soo Kim, Tae Keun Yoo
2025	J	`jnl`	Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction. npj Digit. Medicine Joon Yul Choi, Doo Eun Kim, Sung Jin Kim, Hannuy Choi, Tae Keun Yoo
2025	J	`jnl`	Automated detection of retinal artery occlusion in fundus photography via self-supervised deep learning and multimodal interpretability using a multimodal AI chatbot. Medical Biol. Eng. Comput. Sun Young Ryu, Joon Yul Choi, Tae Keun Yoo
2025	J	`jnl`	Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4. Medical Biol. Eng. Comput. Joon Yul Choi, Tae Keun Yoo
2025	J	`jnl`	Multimodal large language models as assistance for evaluation of thyroid-associated ophthalmopathy. Comput. Biol. Medicine Bo Ram Kim, Joon Yul Choi, Tae Keun Yoo
2025	J	`jnl`	Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea. BMC Medical Informatics Decis. Mak. Na Hyeon Yu, Daeun Shin, Ik Hee Ryu, Tae Keun Yoo, Kyungmin Koh
2024	J	`jnl`	Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era. Medical Biol. Eng. Comput. Joon Yul Choi, Hyungsu Kim, Jin Kuk Kim, In Sik Lee, Ik Hee Ryu, Jung Soo Kim, Tae Keun Yoo
2024	J	`jnl`	Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography. BMC Medical Informatics Decis. Mak. Joon Yul Choi, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Tae Keun Yoo
2024	—	`conf`	Patient-Level Contrastive Learning for Enhanced Biomarker Prediction in Retinal Imaging. DEMI@MICCAI Hyeonmin Kim, Chanyang Seo, Yunnie Cho, Tae Keun Yoo
2022	J	`jnl`	A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs. Comput. Methods Programs Biomed. Tae Keun Yoo, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Hong Kyu Kim
2021	J	`jnl`	Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images. Comput. Methods Programs Biomed. Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim, Ik Hee Ryu, Jin Kuk Kim
2021	J	`jnl`	Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification. Medical Biol. Eng. Comput. Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim
2020	J	`jnl`	A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease. Comput. Biol. Medicine Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim
2020	J	`jnl`	Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks. Comput. Methods Programs Biomed. Tae Keun Yoo, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Jung Sub Kim, Hong Kyu Kim, Joon Yul Choi
2020	J	`jnl`	Toward automated severe pharyngitis detection with smartphone camera using deep learning networks. Comput. Biol. Medicine Tae Keun Yoo, Joon Yul Choi, Younil Jang, Ein Oh, Ik Hee Ryu
2019	J	`jnl`	Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. npj Digit. Medicine Tae Keun Yoo, Ik Hee Ryu, Geunyoung Lee, Youngnam Kim, Jin Kuk Kim, In Sik Lee, Jung Sub Kim, Tyler Hyungtaek Rim
2019	J	`jnl`	The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Medical Biol. Eng. Comput. Tae Keun Yoo, Joon Yul Choi, Jeong Gi Seo, Ramasubramanian Bhoopalan, Selvaperumal Sundaramoorthy, Deok Won Kim
2014	—	`conf`	Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine. EMBC Soo Beom Choi, Jee Soo Park, Jai Won Chung, Tae Keun Yoo, Deok Won Kim
2014	J	`jnl`	Screening for Prediabetes Using Machine Learning Models. Comput. Math. Methods Medicine Soo Beom Choi, Won Jae Kim, Tae Keun Yoo, Jee Soo Park, Jai Won Chung, Yong-Ho Lee, Eun Seok Kang, Deok Won Kim
2013	J	`jnl`	Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study. BMC Medical Informatics Decis. Mak. Ein Oh, Tae Keun Yoo, Eun-Cheol Park
2013	—	`conf`	Interpretation of movement during stair ascent for predicting severity and prognosis of knee osteoarthritis in elderly women using support vector machine. EMBC Tae Keun Yoo, Sung Kean Kim, Soo Beom Choi, Deog Young Kim, Deok Won Kim
2013	J	`jnl`	Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Medical Biol. Eng. Comput. Kyung-Ah Kim, Joon Yul Choi, Tae Keun Yoo, Sung Kean Kim, KilSoo Chung, Deok Won Kim
2013	—	`conf`	Osteoporosis risk prediction using machine learning and conventional methods. EMBC Sung Kean Kim, Tae Keun Yoo, Ein Oh, Deok Won Kim
2013	—	`conf`	Risk Prediction of Femoral Neck Osteoporosis Using Machine Learning and Conventional Methods. IWANN (2) Tae Keun Yoo, Sung Kean Kim, Ein Oh, Deok Won Kim
2012	—	`conf`	A survival prediction model of rats in hemorrhagic shock using the random forest classifier. EMBC Joon Yul Choi, Sung Kean Kim, Wan Hyung Lee, Tae Keun Yoo, Deok Won Kim
2011	—	`conf`	Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine. EMBC Kyung Hwan Jang, Tae Keun Yoo, Joon Yul Choi, Ki Chang Nam, Jae Lim Choi, Min Kyung Kwon, Deok Won Kim

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.