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

Zero-shot learning (ZSL) has received increasing attention in recent years
especially in areas of fine-grained object recognition, retrieval, and image
captioning. The key to ZSL is to transfer knowledge from the seen to the unseen
classes via auxiliary class attribute vectors. However, the popularly learned
projection functions in previous works cannot generalize well since they assume
the distribution consistency between seen and unseen domains at
sample-level.Besides, the provided non-visual and unique class attributes can
significantly degrade the recognition performance in semantic space. In this
paper, we propose a simple yet effective convolutional prototype learning (CPL)
framework for zero-shot recognition. By assuming distribution consistency at
task-level, our CPL is capable of transferring knowledge smoothly to recognize
unseen samples.Furthermore, inside each task, discriminative visual prototypes
are learned via a distance based training mechanism. Consequently, we can
perform recognition in visual space, instead of semantic space. An extensive
group of experiments are then carefully designed and presented, demonstrating
that CPL obtains more favorable effectiveness, over currently available
alternatives under various settings.

本文提出了一种卷积原型学习（CPL）框架，用于零样本识别。通过在任务级别假设分布一致性，CPL 能够平稳地转移知识以识别未见样本。在每个任务内部，通过基于距离的训练机制学习区分性视觉原型。因此，我们可以在视觉空间而不是语义空间中进行识别。

卷积原型学习用于零样本识别

Convolutional Prototype Learning for Zero-Shot Recognition

Convolutional neural networks (CNNs) have been widely used for image
classification. Despite its high accuracies, CNN has been shown to be easily
fooled by some adversarial examples, indicating that CNN is not robust enough
for pattern classification. In this paper, we argue that the lack of robustness
for CNN is caused by the softmax layer, which is a totally discriminative model
and based on the assumption of closed world (i.e., with a fixed number of
categories). To improve the robustness, we propose a novel learning framework
called convolutional prototype learning (CPL). The advantage of using
prototypes is that it can well handle the open world recognition problem and
therefore improve the robustness. Under the framework of CPL, we design
multiple classification criteria to train the network. Moreover, a prototype
loss (PL) is proposed as a regularization to improve the intra-class
compactness of the feature representation, which can be viewed as a generative
model based on the Gaussian assumption of different classes. Experiments on
several datasets demonstrate that CPL can achieve comparable or even better
results than traditional CNN, and from the robustness perspective, CPL shows
great advantages for both the rejection and incremental category learning
tasks.

本文提出了一种新的学习框架，称为卷积原型学习（CPL），以处理卷积神经网络（CNN）对开放世界识别问题的鲁棒性不足，其中采用了多个分类标准进行网络训练，提出了原型损失（PL）作为正则化以改善特征表示的内部类紧凑性，在多个数据集上实验表明 CPL 可以比传统 CNN 实现可比甚至更好的结果，并且 CPL 在拒绝和增量类别学习任务方面具有明显优势。