Enabling Efficient Training of Convolutional Neural Networks for Histopathology Images

Convolutional Neural Networks (CNNs) have gained lots of attention in various digital imaging applications. They have proven to produce incredible results, especially on big data, that require high processing demands. With the increasing size of datasets, especially in computational pathology, CNN processing takes even longer and uses higher computational resources. Considerable research has been conducted to improve the efficiency of CNN, such as quantization. This paper aims to apply efficient training and inference of ResNet using quantization on histopathology images, the Patch Camelyon (PCam) dataset. An analysis for efficient approaches to classify histopathology images is presented. First, the original RGB-colored images are evaluated. Then, compression methods such as channel reduction and sparsity are applied. When comparing sparsity on grayscale with RGB modes, classification accuracy is relatively the same, but the total number of MACs is less in sparsity on grayscale by 77% than RGB. A higher classification result was achieved by grayscale mode, which requires much fewer MACs than the original RGB mode. Our method's low energy and processing make this project suitable for inference on wearable healthcare low powered devices and mobile hospitals in rural areas or developing countries. This also assists pathologists by presenting a preliminary diagnosis.