469 citations · 706 across the 59 of their papers we have counts for
17 papers · 1 filter
Image Generation Diversity Issues and How to Tame Them
Mischa Dombrowski, Weitong Zhang, Sarah Cechnicka +2
Generative methods now produce outputs nearly indistinguishable from real data but often fail to fully capture the data distribution. Unlike quality issues, diversity limitations i…
Uncovering Hidden Subspaces in Video Diffusion Models Using Re-Identification
Mischa Dombrowski, Hadrien Reynaud, Bernhard Kainz
Latent Video Diffusion Models can easily deceive casual observers and domain experts alike thanks to the produced image quality and temporal consistency. Beyond entertainment, this…
Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models
Muhammad Haaris Khan, Hadrien Reynaud, Bernhard Kainz
Although powerful for image generation, consistent and controllable video is a longstanding problem for diffusion models. Video models require extensive training and computational…
JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation
Hadrien Reynaud, Matthew Baugh, Mischa Dombrowski +3
We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos. We achieve this by integrating two diffusion…
Downscaling Neural Network for Coastal Simulations
Zhi-Song Liu, Markus Büttner, Matthew Scarborough +4
Learning the fine-scale details of a coastal ocean simulation from a coarse representation is a challenging task. For real-world applications, high-resolution simulations are neces…
URCDM: Ultra-Resolution Image Synthesis in Histopathology
Sarah Cechnicka, James Ball, Matthew Baugh +6
Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI). However, existing generative methods…
Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks
Johanna P. Müller, Bernhard Kainz
We introduce a fast Self-adapting Forward-Forward Network (SaFF-Net) for medical imaging analysis, mitigating power consumption and resource limitations, which currently primarily…
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
Sergio Naval Marimont, Vasilis Siomos, Matthew Baugh +3
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free.…
Image Distillation for Safe Data Sharing in Histopathology
Zhe Li, Bernhard Kainz
Histopathology can help clinicians make accurate diagnoses, determine disease prognosis, and plan appropriate treatment strategies. As deep learning techniques prove successful in…
Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
Weitong Zhang, Chengqi Zang, Liu Li +3
Inverse problems describe the process of estimating the causal factors from a set of measurements or data. Mapping of often incomplete or degraded data to parameters is ill-posed,…
Weakly Supervised Learning of Cortical Surface Reconstruction from Segmentations
Qiang Ma, Liu Li, Emma C. Robinson +2
Existing learning-based cortical surface reconstruction approaches heavily rely on the supervision of pseudo ground truth (pGT) cortical surfaces for training. Such pGT surfaces ar…
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
Hadrien Reynaud, Qingjie Meng, Mischa Dombrowski +5
To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imag…
The Developing Human Connectome Project: A Fast Deep Learning-based Pipeline for Neonatal Cortical Surface Reconstruction
Qiang Ma, Kaili Liang, Liu Li +6
The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been deve…
Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation
Mathias Öttl, Frauke Wilm, Jana Steenpass +9
Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generat…
Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases
Sophie Starck, Vasiliki Sideri-Lampretsa, Bernhard Kainz +3
Anatomical atlases are widely used for population studies and analysis. Conditional atlases target a specific sub-population defined via certain conditions, such as demographics or…
Arc2Face: A Foundation Model for ID-Consistent Human Faces
Foivos Paraperas Papantoniou, Alexandros Lattas, Stylianos Moschoglou +3
This paper presents Arc2Face, an identity-conditioned face foundation model, which, given the ArcFace embedding of a person, can generate diverse photo-realistic images with an unp…
Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans
Lorenzo Venturini, Samuel Budd, Alfonso Farruggia +6
The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shif…