9 papers
Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture
Zhe Li, Bernhard Kainz
Deep neural networks demonstrate impressive performance in visual recognition but remain highly vulnerable to imperceptible adversarial attacks. Existing defense strategies such as…
InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography
Zhe Li, Hadrien Reynaud, Alberto Gomez +1
Echocardiography plays a critical role in the diagnosis and monitoring of cardiovascular diseases as a non-invasive real-time assessment of cardiac structure and function. However,…
Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis
Zhe Li, Hadrien Reynaud, Johanna P Müller +1
Ultrasound echocardiography is essential for the non-invasive, real-time assessment of cardiac function, but the scarcity of labelled data, driven by privacy restrictions and the c…
Leveraging Multi-Modal Information to Enhance Dataset Distillation
Zhe Li, Hadrien Reynaud, Bernhard Kainz
Dataset distillation aims to create a small and highly representative synthetic dataset that preserves the essential information of a larger real dataset. Beyond reducing storage a…
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…
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…