Scholar
Jin Tae Kwak
Google Scholar ID: NZWxQ-wAAAAJ
Korea University
Citations & Impact
Citations
5,134
H-index
29
i10-index
57
Publications
20
Co-authors
0
Publications
Resume
Academic Achievements
- "CAMP: Continuous and Adaptive Learning Model in Pathology" accepted by npj Artificial Intelligence
- "NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation" accepted by CIBM
- "Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images" accepted for presentation at ICCV2025
- "DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images" accepted by MEDIA
- "Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis" accepted for presentation at MICCAI2025
- "Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios" accepted by CIBM
Research Experience
- Leads or participates in multiple research projects focused on developing cutting-edge artificial intelligence and deep learning techniques to address real-world problems.
Background
- Research interests include Brain Imaging & AI, Large-scale AI Models & Medicine, Hardware-aware Efficient AI, and Domain Invariant AI.
Miscellany
- The lab is looking for enthusiastic & talented graduate students and post-docs; prospective candidates are encouraged to contact Prof. Kwak via email.