Chest X-rays have become the focus of vigorous deep learning research in
recent years due to the availability of large labeled datasets. While
classification of anomalous findings is now possible, ensuring that they are
correctly localized still remains challenging, as this requires recognition of
anomalies within anatomical regions. Existing deep learning networks for
fine-grained anomaly classification learn location-specific findings using
architectures where the location and spatial contiguity information is lost
during the flattening step before classification. In this paper, we present a
new spatially preserving deep learning network that preserves location and
shape information through auto-encoding of feature maps during flattening. The
feature maps, auto-encoder and classifier are then trained in an end-to-end
fashion to enable location aware classification of findings in chest X-rays.
Results are shown on a large multi-hospital chest X-ray dataset indicating a
significant improvement in the quality of finding classification over
state-of-the-art methods.

本研究基于自动编码训练出一种新的深度学习神经网络，能够保留解剖结构和空间位置信息，通过对胸部 X 光片的异常评估，实现精确定位和分类，相较现有方法有了显著提升。

用于胸部 X 光片结果定位分类的空间保留平面化技术

Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays

Location awareness in wireless networks may enable many applications such as
emergency services, autonomous driving and geographic routing. Although there
are many available positioning techniques, none of them is adapted to work with
massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G
technology candidate. In this paper, we discuss possible solutions for
positioning of mobile stations using a vector of signals at the base station,
equipped with many antennas distributed over deployment area. Our main proposal
is to use fingerprinting techniques based on a vector of received signal
strengths. This kind of methods are able to work in highly-cluttered multipath
environments, and require just one base station, in contrast to standard
range-based and angle-based techniques. We also provide a solution for
fingerprinting-based positioning based on Gaussian process regression, and
discuss main applications and challenges.

该篇论文讨论了在使用大量多入多出系统的无线网络中进行定位的可能解决方案，并提出了一种基于接收信号强度向量的指纹识别技术，用于在高度杂波的复杂路径环境下进行站点定位。

分布式大规模 MIMO 系统基于指纹定位技术

Fingerprinting-Based Positioning in Distributed Massive MIMO Systems

Location awareness is a key factor for a wealth of wireless indoor
applications. Its provision requires the careful fusion of diverse information
sources. For agents that use radio signals for localization, this information
may either come from signal transmissions with respect to fixed anchors, from
cooperative transmissions inbetween agents, or from radar-like monostatic
transmissions. Using a-priori knowledge of a floor plan of the environment,
specular multipath components can be exploited, based on a geometric-stochastic
channel model. In this paper, a unified framework is presented for the
quantification of this type of position-related information, using the concept
of equivalent Fisher information. We derive analytical results for the
Cram\'er-Rao lower bound of multipath-assisted positioning, considering
bistatic transmissions between agents and fixed anchors, monostatic
transmissions from agents, cooperative measurements inbetween agents, and
combinations thereof, including the effect of clock offsets. Awareness of this
information enables highly accurate and robust indoor positioning.
Computational results show the applicability of the framework for the
characterization of the localization capabilities of a given environment,
quantifying the influence of different system setups, signal parameters, and
the impact of path overlap.

本文提出了一种使用等效费舍尔信息的综合框架，用于定量化墙面和天花板的反射分路分量所得的位置相关信息。