Invasive Ductal Carcinoma Detection by A Gated Recurrent Unit Network with Self Attention

摘要

Representing around 80% of breast cancer, Invasive Ductal Carcinoma is the most common type of breast cancer. In this work, we have proposed a self-attention GRU model to detect Invasive Ductal Carcinoma. Self-attention is a way to motivate the architecture paying the attention to different locations of the sequence generated by an image effectively mapping regions of the image. The model was used to discriminate between cancerous samples and non-cancerous samples through training on the breast cancer specimens. The ability of discriminative representation has been improved using the self-attention mechanism. We have achieved the best average accuracy of 86%, a mean f1 score of 86% from our proposed model (It should be noted that we used 1:1 train-test split to achieve this score). We also experimented with a baseline CNN, ResNets (ResNet-18, ResNet-34, ResNet-50) and RNN variants (LSTM, LSTM + Attention). Our simple recurrent architectures with the attention mechanism outperformed Convolutional Networks which are traditional choices for image classification tasks. We have demonstrated how the scale of data can play a big role in model selection by studying different RNN, CNN variations for breast cancer detection scheme. This result is expected to be helpful in the early detection of breast cancer.