5 papers
Vision-Language Models as Zero-Annotation Oracles in Histopathology
Vishal Jain, Giorgio Buzzanca, Sarah Cechnicka +6
Foreground segmentation is the critical first step of every computational pathology pipeline, yet existing methods rely on hand-tuned heuristics or supervised models that overfit t…
Graph Conditioned Diffusion for Controllable Histopathology Image Generation
Sarah Cechnicka, Matthew Baugh, Weitong Zhang +5
Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sen…
Dataset Distillation with Probabilistic Latent Features
Zhe Li, Sarah Cechnicka, Cheng Ouyang +3
As deep learning models grow in complexity and the volume of training data increases, reducing storage and computational costs becomes increasingly important. Dataset distillation…
Video Dataset Condensation with Diffusion Models
Zhe Li, Hadrien Reynaud, Mischa Dombrowski +3
In recent years, the rapid expansion of dataset sizes and the increasing complexity of deep learning models have significantly escalated the demand for computational resources, bot…
Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions
Ching-En Chiu, Aditi Roy, Sarah Cechnicka +6
Physics-Informed Neural Networks (PINNs) are fast becoming an important tool to solve differential equations rapidly and accurately, and to identify the systems parameters that bes…