Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis

Recently, convolutional neural networks (CNNs) directly using whole slide images (WSIs) for tumor diagnosis and analysis have attracted considerable attention, because they only utilize the slide-level label for model training without any additional annotations. However, it is still a challenging task to directly handle gigapixel WSIs, due to the billions of pixels and intra-variations in each WSI. To overcome this problem, in this paper, we propose a novel end-to-end interpretable deep MIL framework for WSI analysis, by using a two-branch deep neural network and a multi-scale representation attention mechanism to directly extract features from all patches of each WSI. Specifically, we first divide each WSI into bag-, patch- and cell-level images, and then assign the slide-level label to its corresponding bag-level images, so that WSI classification becomes a MIL problem. Additionally, we design a novel multi-scale representation attention mechanism, and embed it into a two-branch deep network to simultaneously mine the bag with a correct label, the significant patches and their cell-level information. Extensive experiments demonstrate the superior performance of the proposed framework over recent state-of-the-art methods, in term of classification accuracy and model interpretability. All source codes are released at: https://github.com/xhangchen/MRAN/.