title: Binary Segmentation Dataset Distances for Transfer Learning
abstract: This work is devoted to developing a novel transfer learning approach for solving binary semantic segmentation problems that often arise on short samples in the medical (segmentation of nodules in lungs, tumors, polyps, etc.) and other domains.The goal is to optimally select the most suitable dataset from a different subject area with similar feature space and distribution to the target data.Examples show that a severe disadvantage of transfer learning is the difficulty of selecting an initial t
journal: International Journal of Image Graphics and Signal Processing