  An important part of breast cancer staging is the assessment of the sentinel
axillary node for early signs of tumor spreading. However, this assessment by
pathologists is not always easy and retrospective surveys often requalify the
status of a high proportion of sentinel nodes. Convolutional Neural Networks
(CNNs) are a class of deep learning algorithms that have shown excellent
performances in the most challenging visual classification tasks, with numerous
applications in medical imaging. In this study I compare twelve different CNNs
and different hardware acceleration devices for the detection of breast cancer
from microscopic images of breast cancer tissue. Convolutional models are
trained and tested on two public datasets. The first one is composed of more
than 300,000 images of sentinel lymph node tissue from breast cancer patients,
while the second one has more than 220,000 images from inductive breast
carcinoma tissue, one of the most common forms of breast cancer. Four different
hardware acceleration cards were used, with an off-the-shelf deep learning
framework. The impact of transfer learning and hyperparameters fine-tuning are
tested. Hardware acceleration device performance can improve training time by a
factor of five to twelve, 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 and the complexity of the model generally improves performance, but the
relationship is not linear and also depends on the architecture of the model.
The performance of transfer learning is always worse compared to a complete
retraining of the model. Fine-tuning the hyperparameters of the model improves
the results, with the best model showing a performance comparable to
state-of-the-art models.
