5 citations · 13 across the 13 of their papers we have counts for
13 papers
ESVR: 3D Ellipsoid-based Sparse Volume Rendering via Structure-aware Primitive Learning and Per-primitive Ray Sampling
Suemin Jeon, Youjin Kim, Jungwoo Park +2
Efficient representation and rendering of large-scale sparse volumetric data remain challenging in scientific visualization, as meaningful structures often occupy only a small frac…
Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models
Kyungryun Lee, Won-Ki Jeong
Electron microscopy (EM) images exhibit anisotropic axial resolution due to the characteristics inherent to the imaging modality, presenting challenges in analysis and downstream t…
CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement
Seonghui Min, Won-Ki Jeong
Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance i…
Histopathology Image Classification using Deep Manifold Contrastive Learning
Jing Wei Tan, Won-Ki Jeong
Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly used similarity metric in cont…
Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization
Hyun-Jic Oh, Kanggeun Lee, Won-Ki Jeong
Current state-of-the-art supervised deep learning-based segmentation approaches have demonstrated superior performance in medical image segmentation tasks. However, such supervised…
DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
Hyun-Jic Oh, Won-Ki Jeong
Nuclei segmentation and classification is a significant process in pathology image analysis. Deep learning-based approaches have greatly contributed to the higher accuracy of this…
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images
Kanggeun Lee, Kyungryun Lee, Won-Ki Jeong
Although the advances of self-supervised blind denoising are significantly superior to conventional approaches without clean supervision in synthetic noise scenarios, it shows poor…
MitoVis: A Visually-guided Interactive Intelligent System for Neuronal Mitochondria Analysis
JunYoung Choi, Hakjun Lee, Suyeon Kim +2
Neurons have a polarized structure, including dendrites and axons, and compartment-specific functions can be affected by dwelling mitochondria. It is known that the morphology of m…
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation
Khoa Tuan Nguyen, Ganghee Jang, Tran Anh Tuan +1
Segmentation of nanoscale electron microscopy (EM) images is crucial but still challenging in connectomics research. One reason for this is that none of the existing segmentation m…
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising
Kanggeun Lee, Won-Ki Jeong
With the advent of advances in self-supervised learning, paired clean-noisy data are no longer required in deep learning-based image denoising. However, existing blind denoising me…
Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture
Kanggeun Lee, Won-Ki Jeong
With the advent of recent advances in unsupervised learning, efficient training of a deep network for image denoising without pairs of noisy and clean images has become feasible. H…
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency
Hyeonsoo Lee, Won-Ki Jeong
Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation h…
Reinforced Coloring for End-to-End Instance Segmentation
Tuan Tran Anh, Khoa Nguyen-Tuan, Tran Minh Quan +1
Instance segmentation is one of the actively studied research topics in computer vision in which many objects of interest should be separated individually. While many feed-forward…