  A meningioma is a type of brain tumor that requires tumor volume size follow
ups in order to reach appropriate clinical decisions. A fully automated tool
for meningioma detection is necessary for reliable and consistent tumor
surveillance. There have been various studies concerning automated lesion
detection. Studies on the application of convolutional neural network
(CNN)-based methods, which have achieved a state-of-the-art level of
performance in various computer vision tasks, have been carried out. However,
the applicable diseases are limited, owing to a lack of strongly annotated data
being present in medical image analysis. In order to resolve the above issue we
propose pyramid gradient-based class activation mapping (PG-CAM) which is a
novel method for tumor localization that can be trained in weakly supervised
manner. PG-CAM uses a densely connected encoder-decoder-based feature pyramid
network (DC-FPN) as a backbone structure, and extracts a multi-scale Grad-CAM
that captures hierarchical features of a tumor. We tested our model using
meningioma brain magnetic resonance (MR) data collected from the collaborating
hospital. In our experiments, PG-CAM outperformed Grad-CAM by delivering a 23
percent higher localization accuracy for the validation set.
