Multiple Magnification Learning: Breast Tumor Classification with Deep Learning from Histopathological Images Based on Multiple Instance Learning Concept

The Convolutional Neural Network (CNN) is one of the deep learning techniques that allows for the learning of feature representation given only input images. The development of CNNs has been common in many computer vision problems and is recently employed in histopathological image analysis. This study introduces a novel Multiple Magnification Learning (MML) method that leverages the concept of Multiple Instance Learning, integrating it with a sophisticated Convolutional Neural Network architecture. This network is uniquely designed with four separate input streams to concurrently analyze images at four varied levels of magnification, which simultaneously processes images at four different magnification levels. The backbone network used in the model is EfficientNetV2-S for histopathological image classification. The proposed method significantly outperforms previous state-of-the-art approaches in terms of multiple evaluation metrics on an independent test dataset.