Titlebook: Deep Generative Models; Third MICCAI Worksho Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2024 The Editor(s) (if app

Dislocation

不可知論

DNR215

Connotation

Abstract Factory (Abstract Factory),n a Unet by exploiting the DDPM training objective, and then fine-tune the resulting model on a segmentation task. Our experimental results on the segmentation of dental radiographs demonstrate that the proposed method is competitive with state-of-the-art pre-training methods.

PURG

Privacy Distillation: Reducing Re-identification Risk of?Diffusion Modelsy risk; (iii) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with Privacy Distillation can effectively reduce re-identification risk whilst maintaining downstream performance.

absorbed

Investigating Data Memorization in?3D Latent Diffusion Models for?Medical Image Synthesis datasets. To detect potential memorization of training samples, we utilize self-supervised models based on contrastive learning. Our results suggest that such latent diffusion models indeed memorize training data, and there is a dire need for devising strategies to mitigate memorization.

發(fā)炎

樂章

聽覺

全部