Logo: to the web site of Uppsala University

We present how replacing convolutional neural networks with a rotation-equivariant counterpart can be used to reduce the amount of training images needed for classification of whether a cell is cancerous or not. Our hypothesis is that data augmentation schemes by rotation can be replaced, thereby increasing weight sharing and reducing overfitting. The dataset at hand consists of single cell images. We have balanced a subset of almost 9.000 images from healthy patients and patients diagnosed with cancer. Results show that classification accuracy is improved and overfitting reduced if compared to an ordinary convolutional neural network. The results are encouraging and thereby an advancing step towards making screening of patients widely used for the application of oral cancer.

Advances in image segmentation techniques, brought by convolutional neural network (CNN) architectures like U-Net, show promise for tasks such as automated cancer screening. Recently, these methods have been extended to detect different instances of the same class, which could be used to, for example, characterize individual cells in whole-slide images. Still, the amount of data needed and the number of parameters in the network are substantial. To alleviate these problems, we modify a method of semantic instance segmentation to also enforce equivariance to the p4 symmetry group of 90-degree rotations and translations. We perform four experiments on a synthetic dataset of scattered sticks and a subset of the Kaggle 2018 Data Science Bowl, the BBBC038 dataset, consisting of segmented nuclei images. Results indicate that the rotation-equivariant architecture yields similar accuracy as a baseline architecture. Furthermore, we observe that the rotation-equivariant architecture converges faster than the baseline. This is a promising step towards reducing the training time during development of methods based on deep learning.