publications

2024

1. NeurIPS

   Spontaneous symmetry breaking in generative diffusion models

   Raya, Gabriel, and Ambrogioni, Luca

   Advances in Neural Information Processing Systems 2024

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   Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data. In this paper, we show that the dynamics of these models exhibit a spontaneous symmetry breaking that divides the generative dynamics into two distinct phases:1) A linear steady-state dynamics around a central fixed-point and 2) an attractor dynamics directed towards the data manifold. These two "phases" are separated by the change in stability of the central fixed-point, with the resulting window of instability being responsible for the diversity of the generated samples. Using both theoretical and empirical evidence, we show that an accurate simulation of the early dynamics does not significantly contribute to the final generation, since early fluctuations are reverted to the central fixed point. To leverage this insight, we propose a Gaussian late initialization scheme, which significantly improves model performance, achieving up to 3x FID improvements on fast samplers, while also increasing sample diversity (e.g., racial composition of generated CelebA images). Our work offers a new way to understand the generative dynamics of diffusion models that has the potential to bring about higher performance and less biased fast-samplers.

2. MIA

   Diffusion models for out-of-distribution detection in digital pathology

   Linmans, Jasper, Raya, Gabriel, Laak, Jeroen, and Litjens, Geert

   Medical Image Analysis 2024

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   We applied diffusion models for unsupervised out-of-distribution (OOD) detection in digital pathology, significantly outperforming existing methods in the Camelyon16 challenge. These models effectively identify OOD data with high accuracy and alter anomalous input to appear more normal. Despite not reaching supervised learning’s performance, diffusion models show considerable potential for anomaly detection in medical imaging.

2021

1. SPIE

   Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images

   Aswolinskiy, W., Tellez, D., Raya, G., Woude, Lieke, Looijen-Salamon, Monika, Laak, Jeroen, Grunberg, Katrien, and Ciompi, Francesco

   2021

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   Classification of non-small-cell lung cancer (NSCLC) into adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) is essential to select the appropriate treatment for the patient. However, most machine learning approaches require many detailed annotations of whole slide images. We propose to use Neural Image Compression (NIC), which requires only slide-level labels, to classify NSCLC. We show that NIC approaches state of the art performance on lung classification when trained on >2,000 slides from the TCGA and TCIA databases. The models reach AUCs in the range 0.84-0.98 on several internal and external cohorts.

2020

1. MSc. Thesis

   Unsupervised Out-of-distribution detection in digital pathology

   Raya, G.

   2020

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