Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data.
Background/Objectives
Whereas rare cases of hemispatial visual neglect have been reported in patients with a neurodegenerative disease, quadrantic visuospatial neglect has not been described. We report a patient with probable posterior cortical atrophy who demonstrated lower right-sided quadrantic visuospatial neglect, together with allocentric vertical neglect.
Methods/Results
A 68-year-old man initially noted deficits in reading and writing. Subsequently, he developed other cognitive deficits. On vertical line bisections, he deviated upward, and on horizontal line bisections, he deviated to the left. These deviations together suggest that this man’s neglect might be most severe in his right (head/body-centered) lower (below eye level) visual space. When attempting to perform vertical line bisections in all four egocentric quadrants, his upward deviations were largest in the right lower quadrant. On a cancelation test, he revealed bilateral lower (ventral) allocentric neglect but not egocentric neglect. This patient’s magnetic resonance imaging revealed cortical atrophy, most prominent in the left parietal lobe.
Discussion
Previous research in stroke patients has demonstrated that the parietal lobes are important in mediating attention to contralateral and inferior visual space. The presence of left parietal atrophy may have induced this right lower (ventral) egocentric inattention as well as bilateral ventral allocentric inattention. Although to our knowledge there have been no prior reports of a patient with right lower quadrantic and lower vertical allocentric visuospatial neglect, patients are rarely tested for these forms of neglect, and this patient illustrates the importance of evaluating patients for these and other forms of neglect.
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