How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound

• URL: http://arxiv.org/abs/2310.03608v1
• Date: Thu, 5 Oct 2023 15:42:53 GMT
• Title: How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound
• Authors: Menghan Yu, Sourabh Kulhare, Courosh Mehanian, Charles B Delahunt, Daniel E Shea, Zohreh Laverriere, Ishan Shah, Matthew P Horning
• Abstract summary: Adding synthetic training data using generative models offers a low-cost method to deal with the data scarcity challenge. We show that training with both synthetic and real data outperforms training with real data alone.
• Score: 0.3312417881789094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acquiring large quantities of data and annotations is known to be effective for developing high-performing deep learning models, but is difficult and expensive to do in the healthcare context. Adding synthetic training data using generative models offers a low-cost method to deal effectively with the data scarcity challenge, and can also address data imbalance and patient privacy issues. In this study, we propose a comprehensive framework that fits seamlessly into model development workflows for medical image analysis. We demonstrate, with datasets of varying size, (i) the benefits of generative models as a data augmentation method; (ii) how adversarial methods can protect patient privacy via data substitution; (iii) novel performance metrics for these use cases by testing models on real holdout data. We show that training with both synthetic and real data outperforms training with real data alone, and that models trained solely with synthetic data approach their real-only counterparts. Code is available at https://github.com/Global-Health-Labs/US-DCGAN.

Related papers

• Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data? [8.775988650381397]
  Training medical vision-language pre-training models requires datasets with paired, high-quality image-text data. Recent advancements in Large Language Models have made it possible to generate large-scale synthetic image-text pairs. We propose an automated pipeline to build a diverse, high-quality synthetic dataset.
  arXiv  Detail & Related papers  (2024-10-17T13:11:07Z)
• Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks [5.0243930429558885]
  This paper introduces Knowledge Recycling (KR), a pipeline designed to optimise the generation and use of synthetic data for training downstream classifiers. At the heart of this pipeline is Generative Knowledge Distillation (GKD), the proposed technique that significantly improves the quality and usefulness of the information. The results show a significant reduction in the performance gap between models trained on real and synthetic data, with models based on synthetic data outperforming those trained on real data in some cases.
  arXiv  Detail & Related papers  (2024-07-22T10:31:07Z)
• Image Distillation for Safe Data Sharing in Histopathology [10.398266052019675]
  Histopathology can help clinicians make accurate diagnoses, determine disease prognosis, and plan appropriate treatment strategies. As deep learning techniques prove successful in the medical domain, the primary challenges become limited data availability and concerns about data sharing and privacy. We create a small synthetic dataset that encapsulates essential information, which can be shared without constraints. We train a latent diffusion model and construct a new distilled synthetic dataset with a small number of human readable synthetic images.
  arXiv  Detail & Related papers  (2024-06-19T13:19:08Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records [1.338174941551702]
  This study assesses the capability of the Llama 2 LLM to create synthetic medical records that accurately reflect real patient information. We focus on generating synthetic narratives for the History of Present Illness section, utilising data from the MIMIC-IV dataset for comparison. Our findings suggest that this chain-of-thought prompted approach allows the zero-shot model to achieve results on par with those of fine-tuned models, based on Rouge metrics evaluation.
  arXiv  Detail & Related papers  (2024-03-13T16:17:09Z)
• Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data [2.1375651880073834]
  generative AI models have been gaining traction for facilitating open-data sharing. These models generate patient data copies instead of novel synthetic samples. We train 2D and 3D latent diffusion models on CT, MR, and X-ray datasets for synthetic data generation.
  arXiv  Detail & Related papers  (2024-02-01T22:58:21Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.