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%.

本文提出了一种新的基于 3D 深度学习框架的脑部小出血检测方法，不仅可以检测 CMBs，还可以确定它们在脑部的解剖位置，以此显著降低假阳性率并提高检测灵敏度。

利用深度学习实现解剖学尺度定位自动检测微出血，实现完整临床诊断支持

Toward Automated Detection of Microbleeds with Anatomical Scale  Localization: A Complete Clinical Diagnosis Support Using Deep Learning

We propose a novel framework for controllable pathological image synthesis
for data augmentation. Inspired by CycleGAN, we perform cycle-consistent
image-to-image translation between two domains: healthy and pathological.
Guided by a semantic mask, an adversarially trained generator synthesizes
pathology on a healthy image in the specified location. We demonstrate our
approach on an institutional dataset of cerebral microbleeds in traumatic brain
injury patients. We utilize synthetic images generated with our method for data
augmentation in cerebral microbleeds detection. Enriching the training dataset
with synthetic images exhibits the potential to increase detection performance
for cerebral microbleeds in traumatic brain injury patients.

本文提出了一种新的可控病理图像合成的框架，通过使用 CycleGAN、语义掩码和对抗生成器，可以在健康图像中制造出指定区域的病理学信息，并且通过将其用于训练，可以提高颅内出血的检测性能。