Development of a Transfer Learning-Based Interactive Tool for Pathological Image Tissue Segmentation

Key Points

• The tool achieves enhanced efficiency, maintaining accuracy comparable to manual methods despite substantial workload reduction.
• It leverages transfer learning and random forest algorithms to classify tissue image components such as cell nuclei and collagen.
• The evaluation utilized metrics including the Intersection over Union (IoU) and the number of annotation operations performed during testing.
• Future enhancements may focus on incorporating additional shape features to improve accuracy and efficiency in identifying target tissues.