BRIQA: Balanced Reweighting in Image Quality Assessment of Pediatric Brain MRI
Almsouti, Alya Khamitova, Ainur Taratynova, Darya Yaqub, Mohammad
Almsouti, Alya
Khamitova, Ainur
Taratynova, Darya
Yaqub, Mohammad
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Computer Vision
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Conference proceeding
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Abstract
Assessing the severity of artifacts in pediatric brain Magnetic Resonance Imaging (MRI) is critical for diagnostic accuracy, especially in low-field systems where the signal-to-noise ratio is reduced. Manual quality assessment is time-consuming and subjective, motivating the need for robust automated solutions. In this work, we propose BRIQA (Balanced Reweighting in Image Quality Assessment), which addresses class imbalance in artifact severity levels. BRIQA uses gradient-based loss reweighting to dynamically adjust per-class contributions and employs a rotating batching scheme to ensure consistent exposure to underrepresented classes. Through experiments, no single architecture performs best across all artifact types, emphasizing the importance of architectural diversity. The rotating batching configuration improves performance across metrics by promoting balanced learning when combined with cross-entropy loss. BRIQA improves average macro F1 score from 0.659 to 0.706, with notable gains in Noise (0.430), Zipper (0.098), Positioning (0.097), Contrast (0.217), Motion (0.022), and Banding (0.012) artifact severity classification. The code is available at https://github.com/BioMedIA-MBZUAI/BRIQA.
Citation
Source
Lecture Notes in Computer Science
Conference
LISA MICCAI 2025
Keywords
46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation
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LISA MICCAI 2025
Publisher
Springer Nature