Search Results for author:
Found 19 papers, 12 papers with code
Image Generation Diversity Issues and How to Tame Them
Evaluation reveals that current diffusion models converge to limited subsets of the real distribution, with no current state-of-the-art models superpassing 77% of the diversity of the training data.
Uncovering Hidden Subspaces in Video Diffusion Models Using Re-Identification
Our findings indicate that only up to 30. 8% of the training videos are learned in latent video diffusion models, which could explain the lack of performance when training downstream tasks on synthetic data.
Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models
Furthermore, an exploration of the VAE embedding used for latent diffusion models is performed, resulting in interesting theoretical insights such as a method for human-interpretable latent spaces.
JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation
We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos.
URCDM: Ultra-Resolution Image Synthesis in Histopathology
Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI).
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.
Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology
Diagnoses from histopathology images rely on information from both high and low resolutions of Whole Slide Images.
Exploring the Hyperparameter Space of Image Diffusion Models for Echocardiogram Generation
This work presents an extensive hyperparameter search on Image Diffusion Models for Echocardiogram generation.
GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model.
Realistic Data Enrichment for Robust Image Segmentation in Histopathology
Geometric image augmentation is commonly used to improve robustness for average case predictions and to enrich limited datasets.
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.
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.
Is More Data All You Need? A Causal Exploration
Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive.
D'ARTAGNAN: Counterfactual Video Generation
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer
This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging.
Computed Tomography (CT)
Multi-agent Reinforcement Learning
+4
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
We achieve an average frame distance of 3. 36 frames for the ES and 7. 17 frames for the ED on videos of arbitrary length.
