Python Deep Learning Projects

Abstract:

We test a Deep Learning model for 3D medical image segmentation. Our model addresses manual segmentation’s high inter-rater contouring variability and contouring time. The careful analysis can be applied to other medical image segmentation tasks. First, we examine inter-rater detection agreement changes. The model reduces detection disagreements by 48% (p < 0.05). The model improves inter-rater contouring agreement from 0.845 to 0.871 surface Dice Score (p < 0.05). Thirdly, the model accelerates delineation by 1.6–2.0 times (p < 0.05). Finally, we design the clinical experiment to exclude or estimate evaluation biases, preserving the significance of the results. We also discuss building an efficient DL-based model for 3D medical image segmentation.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.

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