Jin Tae Kwak

Associate Professor

Korea University

South Korea

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Jin Tae Kwak is an Associate Professor at Korea University, South Korea. His research interests primarily focus on computational pathology and deep learning applications in medical imaging. He has published extensively on topics such as domain generalization, cancer classification, and innovative methodologies in angiography and image analysis.

Jin Tae Kwak

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Articles (23)

Simultaneous generation of color-coded arteriography, venography, and dynamic angiography: methodology and clinical applications in stroke

Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke

Domain Generalization in Computational Pathology: Survey and Guidelines

DIOR-ViT: Differential ordinal learning Vision Transformer for cancer classification in pathology images

NucleiMix: Realistic data augmentation for nuclei instance segmentation

DeLECA: Deblurring for Long and short Exposure images with a dual-branch multimodal cross attention mechanism

Benchmarking pathology foundation models: Adaptation strategies and scenarios

MoMA: Momentum contrastive learning with multi-head attention-based knowledge distillation for histopathology image analysis

Impact of Collateral Circulation on Futile Endovascular Thrombectomy in Acute Anterior Circulation Ischemic Stroke

3D mobile regression vision transformer for collateral imaging in acute ischemic stroke

DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images

Spatially-constrained and -unconstrained bi-graph interaction network for multi-organ pathology image classification

The CT collateral map: collateral perfusion estimation and baseline lesion assessment after acute anterior circulation ischemic stroke

Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images

CaMeL-Net: Centroid-aware metric learning for efficient multi-class cancer classification in pathology images

Retraction Note: Biparametric MR signal characteristics can predict histopathological measures of prostate cancer

A 3D Multi-task Regression and Ordinal Regression Deep Neural Network for Collateral Imaging from Dynamic Susceptibility Contrast-Enhanced MR perfusion in Acute Ischemic Stroke

SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images

RETRACTED ARTICLE: Biparametric MR signal characteristics can predict histopathological measures of prostate cancer

Multi-Scale Binary Pattern Encoding Network for Cancer Classification in Pathology Images

Region-aggregated attention CNN for disease detection in fruit images

PAIP 2019: Liver cancer segmentation challenge

Semi-supervised learning for an improved diagnosis of COVID-19 in CT images

Collaborators (4)

Misuk Kim

Hyun Jeong Kim

Se Young Chun

JeongJin PARK

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