  We explore the problem of classification within a medical image data-set
based on a feature vector extracted from the deepest layer of pre-trained
Convolution Neural Networks. We have used feature vectors from several
pre-trained structures, including networks with/without transfer learning to
evaluate the performance of pre-trained deep features versus CNNs which have
been trained by that specific dataset as well as the impact of transfer
learning with a small number of samples. All experiments are done on Kimia
Path24 dataset which consists of 27,055 histopathology training patches in 24
tissue texture classes along with 1,325 test patches for evaluation. The result
shows that pre-trained networks are quite competitive against training from
scratch. As well, fine-tuning does not seem to add any tangible improvement for
VGG16 to justify additional training while we observed considerable improvement
in retrieval and classification accuracy when we fine-tuned the Inception
structure.
