A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI

• Published in: Computers in biology and medicine Vol. 174; p. 108414
• Main Authors: Rachmadi, Muhammad Febrian, Byra, Michal, Skibbe, Henrik
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2024; Elsevier Limited
• Subjects: Alzheimer's disease; Biology; Biomedical data; Brain; Brain lesions; Brain research; Computers; Datasets; Ensemble inference; Histopathology; Image processing; Image segmentation; Inference; Instance segmentation; Instance-level detection loss; Instance-level segmentation loss; Magnetic resonance imaging; Medical imaging; Neural networks; Neurodegenerative diseases; Neuroimaging; Object recognition; Pixels; Substantia alba; White matter hyperintensities; Instance-level detection loss; White matter hyperintensities; Brain lesions; Instance-level segmentation loss; Ensemble inference

In this study, we introduce “instance loss functions”, a new family of loss functions designed to enhance the training of neural networks in the instance-level segmentation and detection of objects in biomedical image data, particularly those of varied numbers and sizes. Intended to be utilized conj...