4 papers
Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Images
Hyeseong Lee, Eunsu Kim, D M Bappy +6
Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing essential morphology. Curren…
CountSteer: Steering Attention for Object Counting in Diffusion Models
Hyemin Boo, Hyoryung Kim, Myungjin Lee +4
Text-to-image diffusion models generate realistic and coherent images but often fail to follow numerical instructions in text, revealing a gap between language and visual represent…
Riemannian Geometric-based Meta Learning
JuneYoung Park, YuMi Lee, Tae-Joon Kim +1
Meta-learning, or "learning to learn," aims to enable models to quickly adapt to new tasks with minimal data. While traditional methods like Model-Agnostic Meta-Learning (MAML) opt…
Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic Imaging
Sun-Young Jeon, Sen Wang, Adam S. Wang +2
Fluoroscopy is critical for real-time X-ray visualization in medical imaging. However, low-dose images are compromised by noise, potentially affecting diagnostic accuracy. Noise re…