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In this study I compare different architectures of convolutional neural networks and different hardware acceleration devices for the detection of breast cancer metastasis tissue from microscopic images of sentinel lymph nodes. Convolutional models with increasing depth are trained and tested on a public data set of more than 300,000 images of lymph node tissue, on three different hardware acceleration cards, using an off-the-shelf deep learning framework. The impact of transfer learning, data augmentation and hyperparameters fine-tuning are also tested. Hardware acceleration device performance can improve training time by a factor of five to seven, depending on the model used. On the other hand, increasing convolutional depth will augment the training time by a factor of four to six times, depending on the acceleration device used. Increasing the depth of the model, as could be expected, clearly improves performance, while data augmentation and transfer learning do not. Fine-tuning the hyperparameters of the model notably improves the results, with the best model showing a performance comparable to state-of-the-art models.

Pre-print available here: (https://arxiv.org/abs/2108.13661)