Examples of OCT scan pathological feature masks used as ground truth labels for automated prediction and segmentation of these pathologies by machine learning models.

مواد مشابهة

• Counts of pathologic features segmented on OCT scans for each retinal disease subtype and in total.
  حسب: Katherine Du (20448297)
  منشور في: (2025)
• Evaluation metrics of the five deep learning models for the segmentation of OCT scan pathology using 5-fold cross-validation.
  حسب: Katherine Du (20448297)
  منشور في: (2025)
• Dice coefficients of the five deep learning models for the segmentation of OCT scan pathology using 5-fold cross-validation.
  حسب: Katherine Du (20448297)
  منشور في: (2025)
• Examples of OCT scans manually segmented for each retinal biomarker in the ground truth segmentations, followed by the predicted segmentations from the five deep learning models.
  حسب: Katherine Du (20448297)
  منشور في: (2025)
• Correlation of areas segmented manually (ground truth) versus areas segmented by diffusion and nnU-Net models (predicted area from strongest compared model) per OCT scan.
  حسب: Katherine Du (20448297)
  منشور في: (2025)