Mohammad Javadi’s research while affiliated with University of Houston and other places

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Publications (2)


Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI
  • Article

July 2024

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10 Reads

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2 Citations

Mohammad Javadi

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Nikolaos V. Tsekos

Adversarial training has attracted much attention in enhancing the visual realism of images, but its efficacy in clinical imaging has not yet been explored. This work investigated adversarial training in a clinical context, by training 206 networks on the OASIS-1 dataset for improving low-resolution and low signal-to-noise ratio (SNR) magnetic resonance images. Each network corresponded to a different combination of perceptual and adversarial loss weights and distinct learning rate values. For each perceptual loss weighting, we identified its corresponding adversarial loss weighting that minimized structural disparity. Each optimally weighted adversarial loss yielded an average SSIM reduction of 1.5%. We further introduced a set of new metrics to assess other clinically relevant image features: Gradient Error (GE) to measure structural disparities; Sharpness to compute edge clarity; and Edge-Contrast Error (ECE) to quantify any distortion of the pixel distribution around edges. Including adversarial loss increased structural enhancement in visual inspection, which correlated with statistically consistent GE reductions (p-value << 0.05). This also resulted in increased Sharpness; however, the level of statistical significance was dependent on the perceptual loss weighting. Additionally, adversarial loss yielded ECE reductions for smaller perceptual loss weightings, while showing non-significant increases (p-value >> 0.05) when these weightings were higher, demonstrating that the increased Sharpness does not adversely distort the pixel distribution around the edges in the image. These studies clearly suggest that adversarial training significantly improves the performance of an MRI enhancement pipeline, and highlights the need for systematic studies of hyperparameter optimization and investigation of alternative image quality metrics.


Citations (1)


... Beyond human perceptual studies, advanced evaluation methods, such as eye-tracking and physiological metrics like heart rate variability, will be explored to gain deeper insights into user responses to transformed scenes. Statistical analyses will also be performed to validate the significance of observed improvements and establish the practical relevance of the results such as done by Sharma et al. [43], [44] and Javadi et al. [45]. By addressing these aspects, future work aims to refine the proposed pipeline further and extend its applicability to a broader range of real-world scenarios and practical use cases. ...

Reference:

Deep Learning Approach for Realistic Traffic VideoChanges Across Lighting and Weather Conditions
Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI
  • Citing Article
  • July 2024