Deep learning approaches for data augmentation in medical imaging: a review
A Kebaili, J Lapuyade-Lahorgue, S Ruan - Journal of Imaging, 2023 - mdpi.com
Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …
availability of training data remains a major challenge, particularly in the medical field where …
Extracting training data from diffusion models
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …
significant attention due to their ability to generate high-quality synthetic images. In this work …
Dream the impossible: Outlier imagination with diffusion models
Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …
Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions
Image data augmentation constitutes a critical methodology in modern computer vision
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …
Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images
Despite continued advancement in recent years, deep neural networks still rely on large
amounts of training data to avoid overfitting. However, labeled training data for real-world …
amounts of training data to avoid overfitting. However, labeled training data for real-world …
Beware of diffusion models for synthesizing medical images-a comparison with GANs in terms of memorizing brain MRI and chest x-ray images
Diffusion models were initially developed for text-to-image generation and are now being
utilized to generate high quality synthetic images. Preceded by GANs, diffusion models have …
utilized to generate high quality synthetic images. Preceded by GANs, diffusion models have …
Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation
The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts
ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying …
ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying …
Dipper: Diffusion-based 2d path planner applied on legged robots
J Liu, M Stamatopoulou… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
In this work, we present DiPPeR, a novel and fast 2D path planning framework for
quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a …
quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a …
A diffusion model predicts 3d shapes from 2d microscopy images
Diffusion models are a special type of generative model, capable of synthesising new data
from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the …
from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the …