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%.

翻译：脑微出血（CMBs）是脑组织中慢性沉积的微小血液产物，其与多种脑血管疾病（包括认知衰退、脑内出血和脑梗死）的解剖位置存在明确关联。然而，由于CMB结构稀疏且微小，人工检测是一个耗时且易出错的过程。CMB检测常受钙化、软脑膜血管等大量类CMB伪影影响，导致假阳性率（FPR）过高。本文提出一种新颖的三维深度学习框架，不仅能够检测CMB，还能识别其在脑内的解剖位置（即脑叶、深部及幕下区域）。针对CMB检测任务，我们通过以U-Net为骨干网络结合区域建议网络（RPN）构建单一端到端模型。为在单个模型中显著降低假阳性，我们开发了新方案：包含利用上下文信息检测微小候选病灶的特征融合模块（FFM），以及通过卷积原型学习（CPL）挖掘类CMB伪影并生成附加损失项（称为集中损失）的困难样本原型学习（HSPL）。解剖定位任务不仅能指示CMB所属区域，还能通过利用解剖信息消除检测任务中的部分假阳性。结果表明，采用FFM和HSPL的改进版RPN在灵敏度（94.66% vs. 93.33%）和平均每例假阳性数（FPavg：0.86 vs. 14.73）上均优于基础RPN。同时，解剖定位任务进一步提升了检测性能，将FPavg降至0.56，同时保持了94.66%的灵敏度。