Transfer learning for cancer diagnosis in histopathological images
This work addresses cancer diagnosis in medical imaging by evaluating transfer learning methods, but it is incremental as it focuses on comparing existing architectures without introducing new techniques.
The paper compared 14 pre-trained ImageNet models on a histopathological cancer detection dataset, finding that DenseNet161 achieved high precision and ResNet101 high recall, with transfer learning enabling faster convergence.
Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task. In modern computer vision research, the question is which architecture performs better for a given dataset. In this paper, we compare the performance of 14 pre-trained ImageNet models on the histopathologic cancer detection dataset, where each model has been configured as a naive model, feature extractor model, or fine-tuned model. Densenet161 has been shown to have high precision whilst Resnet101 has a high recall. A high precision model is suitable to be used when follow-up examination cost is high, whilst low precision but a high recall/sensitivity model can be used when the cost of follow-up examination is low. Results also show that transfer learning helps to converge a model faster.