Ge Jin - RankMe

14 papers Journal 13Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Enhancing feature discrimination with pseudo-labels for foundation model in segmentation of 3D medical images. Neural Networks Ge Jin, Qian Zhang, Yong Cheng, Ming Xu, Yingwen Zhu, De Yu, Yongqi Yuan, Juncheng Li, Jun Shi
2026	J	`jnl`	Mathematical and Algorithmic Advances in Machine Learning for Statistical Process Control: A Systematic Review. Entropy Yulong Qiao, Tingting Han, Zixing Wu, Ge Jin, Qian Zhang, Qin Xu
2025	J	`jnl`	HGMSurvNet: A two-stage hypergraph learning network for multimodal cancer survival prediction. Medical Image Anal. Saisai Ding, Linjin Li, Ge Jin, Jun Wang, Shihui Ying, Jun Shi
2025	J	`jnl`	Multi-resolution based dual-channel UNet with cross clique for medical image dense prediction. Expert Syst. Appl. Xueying Zhou, Ge Jin, Yang Liu, Juncheng Li, Jun Wang, Shihui Ying, Yanyan Zheng, Jun Shi
2025	J	`jnl`	Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length. Algorithms Yulong Qiao, Zixing Wu, Qian Zhang, Qin Xu, Ge Jin
2025	J	`jnl`	Topological GCN Guided Improved Conformer for Detection of Hip Landmarks From Ultrasound Images. IEEE J. Biomed. Health Informatics Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Qian Wang, Jun Du, Jun Shi
2024	J	`jnl`	B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion. Comput. Biol. Medicine Xiao Wang, Xinping Ren, Ge Jin, Shihui Ying, Jun Wang, Juncheng Li, Jun Shi
2024	J	`jnl`	BPT-PLR: A Balanced Partitioning and Training Framework with Pseudo-Label Relaxed Contrastive Loss for Noisy Label Learning. Entropy Qian Zhang, Ge Jin, Yi Zhu, Hongjian Wei, Qiu Chen
2024	J	`jnl`	Cross-to-merge training with class balance strategy for learning with noisy labels. Expert Syst. Appl. Qian Zhang, Yi Zhu, Ming Yang, Ge Jin, Yingwen Zhu, Qiu Chen
2024	J	`jnl`	Dual-domain MIM based contrastive learning for CAD of developmental dysplasia of the hip with ultrasound images. Biomed. Signal Process. Control. Ke Sun, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Jun Du, Jun Shi
2024	—	`conf`	Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images. MICCAI (5) Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Jun Du, Jun Shi
2024	J	`jnl`	Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images. CoRR Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Jun Du, Jun Shi
2022	J	`jnl`	An efficient deep neural network framework for COVID-19 lung infection segmentation. Inf. Sci. Ge Jin, Chuancai Liu, Xu Chen
2021	J	`jnl`	Adversarial network integrating dual attention and sparse representation for semi-supervised semantic segmentation. Inf. Process. Manag. Ge Jin, Chuancai Liu, Xu Chen

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.