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

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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.

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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.

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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.

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