Transfer Learning Pre-training Dataset and Fine-tuning Effect Analysis on Cancer Histopathology Images

Due to the shortage of training data, transfer learning is frequently used in constructing medical imaging models. In this study, we perform transfer learning pre-training dataset and fine-tuning effect analysis in cancer histopathology imaging by evaluating three popular deep neural network algorithms on three target datasets under various fine-tuning configurations. Pre-training models with cancer histopathology image datasets appear to perform worse or not better than pre-training models with ImageNet or random initialization. Furthermore, this study demonstrates that the performance of pre-trained models improves with the increase of images used in fine-tuning, which was previously overlooked.