We explore the problem of classification within a medical image data-setbased on a feature vector extracted from the deepest layer of pre-trainedConvolution Neural Networks. We have used feature vectors from severalpre-trained structures, including networks with/without transfer learning toevaluate the performance of pre-trained deep features versus CNNs which havebeen trained by that specific dataset as well as the impact of transferlearning with a small number of samples. All experiments are done on KimiaPath24 dataset which consists of 27,055 histopathology training patches in 24tissue texture classes along with 1,325 test patches for evaluation. The resultshows that pre-trained networks are quite competitive against training fromscratch. As well, fine-tuning does not seem to add any tangible improvement forVGG16 to justify additional training while we observed considerable improvementin retrieval and classification accuracy when we fine-tuned the Inceptionstructure.
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