# Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks Source: [Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:47:17.894Z Authors: Brady Kieffer, Morteza Babaie, Shivam Kalra, H.R.Tizhoosh Published: 2017-10-11T00:02:53.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()