An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.

ABSTRACT: Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we propose a novel neural network model to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level

SUBMITTER: Shen Y 

PROVIDER: S-EPMC7828643 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature