MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part
  Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Binghua Li Jie Mao Zhe Sun Chao Li Qibin Zhao Toshihisa Tanaka
Automated diagnosis with artificial intelligence has emerged as a promising
area in the realm of medical imaging, while the interpretability of the
introduced deep neural networks still remains an urgent concern. Although
contemporary works, such as XProtoNet and MProtoNet, has sought to design
interpretable prediction models for the issue, the localization precision of
their resulting attribution maps can be further improved. To this end, we
propose a Multi-scale Attentive Prototypical part Network, termed MAProtoNet,
to provide more precise maps for attribution. Specifically, we introduce a
concise multi-scale module to merge attentive features from quadruplet
attention layers, and produces attribution maps. The proposed quadruplet
attention layers can enhance the existing online class activation mapping loss
via capturing interactions between the spatial and channel dimension, while the
multi-scale module then fuses both fine-grained and coarse-grained information
for precise maps generation. We also apply a novel multi-scale mapping loss for
supervision on the proposed multi-scale module. Compared to existing
interpretable prototypical part networks in medical imaging, MAProtoNet can
achieve state-of-the-art performance in localization on brain tumor
segmentation (BraTS) datasets, resulting in approximately 4% overall
improvement on activation precision score (with a best score of 85.8%), without
using additional annotated labels of segmentation. Our code will be released in
https://github.com/TUAT-Novice/maprotonet.