Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation

• URL: http://arxiv.org/abs/2407.21674v1
• Date: Wed, 31 Jul 2024 15:14:17 GMT
• Title: Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation
• Authors: Krishan Agyakari Raja Babu, Rachana Sathish, Mrunal Pattanaik, Rahul Venkataramani,
• Abstract summary: Synthetic data is becoming increasingly integral in data-scarce fields such as medical imaging. downstream neural networks often exploit spurious distinctions between real and synthetic data when there is a strong correlation between the data source and the task label. This exploitation manifests as textitsimplicity bias, where models overly rely on superficial features rather than genuine task-related complexities.
• Score: 0.7499722271664144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthetic data is becoming increasingly integral in data-scarce fields such as medical imaging, serving as a substitute for real data. However, its inherent statistical characteristics can significantly impact downstream tasks, potentially compromising deployment performance. In this study, we empirically investigate this issue and uncover a critical phenomenon: downstream neural networks often exploit spurious distinctions between real and synthetic data when there is a strong correlation between the data source and the task label. This exploitation manifests as \textit{simplicity bias}, where models overly rely on superficial features rather than genuine task-related complexities. Through principled experiments, we demonstrate that the source of data (real vs.\ synthetic) can introduce spurious correlating factors leading to poor performance during deployment when the correlation is absent. We first demonstrate this vulnerability on a digit classification task, where the model spuriously utilizes the source of data instead of the digit to provide an inference. We provide further evidence of this phenomenon in a medical imaging problem related to cardiac view classification in echocardiograms, particularly distinguishing between 2-chamber and 4-chamber views. Given the increasing role of utilizing synthetic datasets, we hope that our experiments serve as effective guidelines for the utilization of synthetic datasets in model training.

Related papers

• Marginal Causal Flows for Validation and Inference [3.547529079746247]
  Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging. We introduce Frugal Flows, a novel likelihood-based machine learning model that uses normalising flows to flexibly learn the data-generating process. We demonstrate the above with experiments on both simulated and real-world datasets.
  arXiv  Detail & Related papers  (2024-11-02T16:04:57Z)
• Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data [2.6016285265085526]
  Student models show a significant drop in accuracy compared to models trained on real data. By training these layers using either real or synthetic data, we reveal that the drop mainly stems from the model's final layers. Our results suggest an improved trade-off between the amount of real training data used and the model's accuracy.
  arXiv  Detail & Related papers  (2024-05-06T07:51:13Z)
• The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data [40.165159490379146]
  We show that the rate of false-positive findings (type 1 error) will be unacceptably high, even when the estimates are unbiased. Despite the use of a previously proposed correction factor, this problem persists for deep generative models.
  arXiv  Detail & Related papers  (2023-12-13T02:04:41Z)
• Boosting Data Analytics With Synthetic Volume Expansion [3.568650932986342]
  This article explores the effectiveness of statistical methods on synthetic data and the privacy risks of synthetic data. A key finding within this framework is the generational effect, which reveals that the error rate of statistical methods on synthetic data decreases with the addition of more synthetic data but may eventually rise or stabilize.
  arXiv  Detail & Related papers  (2023-10-27T01:57:27Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Stubborn Lexical Bias in Data and Models [50.79738900885665]
  We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data. We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations. Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
  arXiv  Detail & Related papers  (2023-06-03T20:12:27Z)
• Knowing the Distance: Understanding the Gap Between Synthetic and Real Data For Face Parsing [0.0]
  We show that the distribution gap is the largest contributor to the performance gap, accounting for over 50% of the gap. This suggests that synthetic data is a viable alternative to real data, especially when real data is limited or difficult to obtain.
  arXiv  Detail & Related papers  (2023-03-27T13:59:26Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• 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)
• Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data [63.15776078733762]
  We propose Amortized Causal Discovery, a novel framework to learn to infer causal relations from time-series data. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance.
  arXiv  Detail & Related papers  (2020-06-18T19:59:12Z)

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.