  In clinical practice, human radiologists actually review medical images with
high resolution monitors and zoom into region of interests (ROIs) for a
close-up examination. Inspired by this observation, we propose a hierarchical
graph neural network to detect abnormal lesions from medical images by
automatically zooming into ROIs. We focus on mammogram analysis for breast
cancer diagnosis for this study. Our proposed network consist of two graph
attention networks performing two tasks: (1) node classification to predict
whether to zoom into next level; (2) graph classification to classify whether a
mammogram is normal/benign or malignant. The model is trained and evaluated on
INbreast dataset and we obtain comparable AUC with state-of-the-art methods.
