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

Radiologists usually observe anatomical regions of chest X-ray images as well
as the overall image before making a decision. However, most existing deep
learning models only look at the entire X-ray image for classification, failing
to utilize important anatomical information. In this paper, we propose a novel
multi-label chest X-ray classification model that accurately classifies the
image finding and also localizes the findings to their correct anatomical
regions. Specifically, our model consists of two modules, the detection module
and the anatomical dependency module. The latter utilizes graph convolutional
networks, which enable our model to learn not only the label dependency but
also the relationship between the anatomical regions in the chest X-ray. We
further utilize a method to efficiently create an adjacency matrix for the
anatomical regions using the correlation of the label across the different
regions. Detailed experiments and analysis of our results show the
effectiveness of our method when compared to the current state-of-the-art
multi-label chest X-ray image classification methods while also providing
accurate location information.

本文提出了一种多标签胸部 X 光分类模型，能够准确分类图像发现，同时将发现位置定位到正确的解剖区域。该模型包括两个模块 - 检测模块和解剖依赖模块，并采用图卷积网络实现解剖部位之间的有关系学习。实验结果表明，与现有的多标签胸部 X 射线图像分类方法相比，该方法是有效且能够提供精确的位置信息。