RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 173 - 185

Published: Jan. 1, 2025

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Synthetic tabular data generation in Federated Learning environments: A practical use case for Acute Myeloid Leukemia (Preprint)

Published: March 20, 2025

Data scarcity and dispersion pose significant obstacles in biomedical research, particularly when addressing rare diseases. In such scenarios, Synthetic Generation (SDG) has emerged as a promising path to mitigate the first issue. Concurrently, Federated Learning (FL) is machine learning paradigm where multiple nodes collaborate create centralized model with knowledge that distilled from data different nodes, but without need for sharing it. This research explores combination of SDG FL technologies context Acute Myeloid Leukemia, hematological disorder, evaluating their combined impact quality generated artificial datasets. To evaluate privacy- fidelity-related federating distribution scenarios numbers comparing them baseline model. A state-of-the-art Generative Adversarial Network architecture was trained considering four scenarios: (1) non-federated all available, (2) federated scenario evenly distributed among (3) unevenly randomly (imbalanced data), (4) non-IID distributions. For each fixed set node quantities (3, 5, 7, 10) considered assess its impact, evaluated attending fidelity-privacy trade-off. The computed fidelity metrics exhibited statistically deteriorations (P < 0.001) ranging 0.21% 21.23% due federation process. When experiments diverse no strong tendencies were observed, even if specific comparisons resulted significative differences. Privacy mainly maintained while obtaining maximum improvements 55.17% 26.23, although they not significant. Within scope use case this paper, act an algorithm results loss compared maintaining privacy levels. However, deterioration does significantly increase number used train models grows, though differences found comparisons. fact amount differently neither most nor metrics, similar scenarios.

Language: Английский

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Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: Oct. 20, 2024

Language: Английский

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Optimization research and analysis of the basic pathway of diffusion model based on big data algorithm

Published: July 19, 2024

Language: Английский

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Generative Artificial Intelligence Approaches for Synthesizing High-Fidelity Breast Thermal Images

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 33 - 43

Published: Nov. 2, 2024

Language: Английский

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Synthetic dual image generation for reduction of labeling efforts in semantic segmentation of micrographs with a customized metric function

Methods in microscopy, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 13, 2024

Abstract Training of semantic segmentation models for material analysis requires micrographs as the inputs and their corresponding masks. In this scenario, it is quite unlikely that perfect masks will be drawn, especially at edges objects, sometimes amount data can obtained small, since only a few samples are available. These aspects make very problematic to train robust model. Therefore, we demonstrate in work an easy-to-apply workflow improvement through generation synthetic microstructural images conjunction with The joining respective create input Vector Quantised-Variational AutoEncoder (VQ-VAE) model includes embedding space, which trained such generative (PixelCNN) learns distribution each input, transformed into discrete codes, used sample new codes. latter eventually decoded by VQ-VAE generate alongside segmentation. To evaluate quality generated data, have U-Net different amounts these real data. were then evaluated using microscopic only. Additionally, introduce customized metric derived from mean Intersection over Union (mIoU) excludes classes not part ground-truth mask when calculating mIoU all classes. proposed prevents falsely predicted pixels greatly reducing value mIoU. With implemented workflow, able achieve time reduction preparation acquisition, well image processing labeling tasks. idea behind approach could generalized various types serves user-friendly solution training smaller number images.

Language: Английский

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Synthetic Image Generation of Aortic Valves Using Conditional DDPM

Published: Sept. 13, 2024

Language: Английский

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