This paper presents our contribution to the REFUGE challenge 2020. The
challenge consisted of three tasks based on a dataset of retinal images:
Segmentation of optic disc and cup, classification of glaucoma, and
localization of fovea. We propose employing convolutional neural networks for
all three tasks. Segmentation is performed using a U-Net, classification is
performed by a pre-trained InceptionV3 network, and fovea detection is
performed by employing stacked hour-glass for heatmap prediction. The challenge
dataset contains images from three different data sources. To enhance
performance, cycleGANs were utilized to create a domain-shift between the data
sources. These cycleGANs move images across domains, thus creating artificial
images which can be used for training.

本文使用卷积神经网络，包括 U-Net，InceptionV3 和叠层沙漏网络，针对 REFUGE 2020 的三项任务，视盘和杯的分割，青光眼的分类和中心凹的定位进行研究。为了增强性能，使用了 cycleGANs 来进行域漂移。

EyeLoveGAN：利用循环 GAN 的领域偏移来提升网络学习

EyeLoveGAN: Exploiting domain-shifts to boost network learning with cycleGANs

In the autonomous driving domain, data collection and annotation from real
vehicles are expensive and sometimes unsafe. Simulators are often used for data
augmentation, which requires realistic sensor models that are hard to formulate
and model in closed forms. Instead, sensors models can be learned from real
data. The main challenge is the absence of paired data set, which makes
traditional supervised learning techniques not suitable. In this work, we
formulate the problem as image translation from unpaired data and employ
CycleGANs to solve the sensor modeling problem for LiDAR, to produce realistic
LiDAR from simulated LiDAR (sim2real). Further, we generate high-resolution,
realistic LiDAR from lower resolution one (real2real). The LiDAR 3D point cloud
is processed in Bird-eye View and Polar 2D representations. The experimental
results show a high potential of the proposed approach.

通过 CycleGANs 从未配对的数据中进行图像转换，以实现从模拟 LiDAR 到真实 LiDAR（sim2real）的传感器建模，从低分辨率到高分辨率的真实 LiDAR（real2real）的生成以及使用鸟瞰图和极坐标 2D 表示法处理 LiDAR 3D 点云。实验结果表明，该方法具有很高的潜力。

自动驾驶中基于 GAN 的 LiDAR 传感器建模和数据增强

LiDAR Sensor modeling and Data augmentation with GANs for Autonomous  driving

Image synthesis and image-to-image translation are two important generative
learning tasks. Remarkable progress has been made by learning Generative
Adversarial Networks (GANs)~\cite{goodfellow2014generative} and
cycle-consistent GANs (CycleGANs)~\cite{zhu2017unpaired} respectively. This
paper presents a method of learning Spatial Pyramid Attentive Pooling (SPAP)
which is a novel architectural unit and can be easily integrated into both
generators and discriminators in GANs and CycleGANs. The proposed SPAP
integrates Atrous spatial pyramid~\cite{chen2018deeplab}, a proposed cascade
attention mechanism and residual connections~\cite{he2016deep}. It leverages
the advantages of the three components to facilitate effective end-to-end
generative learning: (i) the capability of fusing multi-scale information by
ASPP; (ii) the capability of capturing relative importance between both spatial
locations (especially multi-scale context) or feature channels by attention;
(iii) the capability of preserving information and enhancing optimization
feasibility by residual connections. Coarse-to-fine and fine-to-coarse SPAP are
studied and intriguing attention maps are observed in both tasks. In
experiments, the proposed SPAP is tested in GANs on the Celeba-HQ-128
dataset~\cite{karras2017progressive}, and tested in CycleGANs on the
Image-to-Image translation datasets including the Cityscape
dataset~\cite{cordts2016cityscapes}, Facade and Aerial Maps
dataset~\cite{zhu2017unpaired}, both obtaining better performance.

本文介绍了一种基于 SPAP 的建筑单元的学习方法，该方法可以轻松地集成到 GAN 和 CycleGAN 的生成器和鉴别器中，以促进有效的端到端生成式学习，在 Celeba-HQ-128 和 Image-to-Image 翻译数据集上得到了更好的性能。

图像合成和图像到图像翻译中的学习空间金字塔注意池化

Learning Spatial Pyramid Attentive Pooling in Image Synthesis and  Image-to-Image Translation

Digitized Histological diagnosis is in increasing demand. However, color
variations due to various factors are imposing obstacles to the diagnosis
process. The problem of stain color variations is a well-defined problem with
many proposed solutions. Most of these solutions are highly dependent on a
reference template slide. We propose a deep-learning solution inspired by
CycleGANs that is trained end-to-end, eliminating the need for an expert to
pick a representative reference slide. Our approach showed superior results
quantitatively and qualitatively against the state of the art methods (10%
improvement visually using SSIM). We further validated our method on a clinical
use-case, namely Breast Cancer tumor classification, showing 12% increase in
AUC. The code will be made publicly available.

利用深度学习方法的 CycleGANs，不需要专家选择代表性的参考标本，解决了数字化组织学诊断中由于多种因素引起的染色剂颜色变化引起的难题，并在乳腺癌肿瘤分类等临床应用中得到了验证。