Stride Independent Patching for Deep Learning
Semi-automatic stride-independent patching (SSP) was developed to improve deep learning model training by positioning patches around user-selected objects of interest instead of using fixed strides. The tool allows users to manually mark areas, and a script extracts patches from these points. Results show SSP provides a good balance between segmentation accuracy and training speed, achieving the highest average precision and F1-score across tested models.