Transfer Learning Pre-training Dataset and Fine-tuning Effect Analysis on Cancer Histopathology Images
Koushik Chandra Howlader, Lu Liu · 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2022
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.