Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
This work addresses classification and segmentation for digital pathology, an incremental improvement in a domain-specific field.
The paper tackled the problem of digital pathology image analysis, which faces challenges due to large image sizes and limited training data, by using CNNs with patch classification and ensembling, achieving 90% accuracy on a breast cancer histology dataset.
In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably, digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available. In this paper, we adopt state-of-the-art convolutional neural networks (CNN) architectures for digital pathology images analysis. We propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original images. To validate the developed approaches, we conducted experiments with \textit{Breast Cancer Histology Challenge} dataset and obtained 90\% accuracy for the 4-class tissue classification task.