  Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in
the brain tissues, which have explicit relation to various cerebrovascular
diseases depending on their anatomical location, including cognitive decline,
intracerebral hemorrhage, and cerebral infarction. However, manual detection of
CMBs is a time-consuming and error-prone process because of their sparse and
tiny structural properties. The detection of CMBs is commonly affected by the
presence of many CMB mimics that cause a high false-positive rate (FPR), such
as calcification and pial vessels. This paper proposes a novel 3D deep learning
framework that does not only detect CMBs but also inform their anatomical
location in the brain (i.e., lobar, deep, and infratentorial regions). For the
CMB detection task, we propose a single end-to-end model by leveraging the
U-Net as a backbone with Region Proposal Network (RPN). To significantly reduce
the FPs within the same single model, we develop a new scheme, containing
Feature Fusion Module (FFM) that detects small candidates utilizing contextual
information and Hard Sample Prototype Learning (HSPL) that mines CMB mimics and
generates additional loss term called concentration loss using Convolutional
Prototype Learning (CPL). The anatomical localization task does not only tell
to which region the CMBs belong but also eliminate some FPs from the detection
task by utilizing anatomical information. The results show that the proposed
RPN that utilizes the FFM and HSPL outperforms the vanilla RPN and achieves a
sensitivity of 94.66% vs. 93.33% and an average number of false positives per
subject (FPavg) of 0.86 vs. 14.73. Also, the anatomical localization task
further improves the detection performance by reducing the FPavg to 0.56 while
maintaining the sensitivity of 94.66%.
