Search Results for author:
Found 9 papers, 7 papers with code
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
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 imaging data.
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in their training.
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting
Recent advances in score-based generative models have led to a huge spike in the development of downstream applications using generative models ranging from data augmentation over image and video generation to anomaly detection.
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-trained models using domain-specific text-to-image datasets.
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis.
Out of Distribution (OOD) Detection
Self-Supervised Anomaly Detection
+1
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
So far, video generation has only been possible by providing input data that is as rich as the output data, e. g., image sequence plus conditioning in, video out.
Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models
Curating datasets for object segmentation is a difficult task.
Zero-Shot Object Segmentation through Concept Distillation from Generative Image Foundation Models
Curating datasets for object segmentation is a difficult task.
D'ARTAGNAN: Counterfactual Video Generation
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions.
