For a unified analysis of medical images from different modalities, data
harmonization using image-to-image (I2I) translation is desired. We study this
problem employing an optical coherence tomography (OCT) data set of
Spectralis-OCT and Home-OCT images. I2I translation is challenging because the
images are unpaired, and a bijective mapping does not exist due to the
information discrepancy between both domains. This problem has been addressed
by the Contrastive Learning for Unpaired I2I Translation (CUT) approach, but it
reduces semantic consistency. To restore the semantic consistency, we support
the style decoder using an additional segmentation decoder. Our approach
increases the similarity between the style-translated images and the target
distribution. Importantly, we improve the segmentation of biomarkers in
Home-OCT images in an unsupervised domain adaptation scenario. Our data
harmonization approach provides potential for the monitoring of diseases, e.g.,
age related macular disease, using different OCT devices.

通过图像到图像（I2I）转换对不同模态的医学图像进行统一分析，解决因信息差异导致的非成对映射挑战。为了恢复语义一致性，本研究在对比学习非成对 I2I 转换（CUT）方法的基础上，通过额外的分割解码器支持风格解码器，提高样式转换图像与目标分布的相似度，并在无监督领域适应的情景中改善生物标志物在 Home-OCT 图像中的分割，为疾病监测（如年龄相关黄斑病变）提供潜力。

光学相干断层扫描图像的对比非配对图像转换的解剖条件

Anatomical Conditioning for Contrastive Unpaired Image-to-Image  Translation of Optical Coherence Tomography Images

Federated Learning is a distributed machine learning approach that enables
geographically distributed data silos to collaboratively learn a joint machine
learning model without sharing data. Most of the existing work operates on
unstructured data, such as images or text, or on structured data assumed to be
consistent across the different sites. However, sites often have different
schemata, data formats, data values, and access patterns. The field of data
integration has developed many methods to address these challenges, including
techniques for data exchange and query rewriting using declarative schema
mappings, and for entity linkage. Therefore, we propose an architectural vision
for an end-to-end Federated Learning and Integration system, incorporating the
critical steps of data harmonization and data imputation, to spur further
research on the intersection of data management information systems and machine
learning.

本文提出了一个面向端到端联合机器学习和数据集成的体系结构愿景，为数据管理信息系统和机器学习交叉研究带来重要启示，以解决不同站点之间存在的数据集成和数据格式一致性问题。

基于协调数据孤岛的联邦学习

Federated Learning over Harmonized Data Silos

In this paper, we introduce multi-task learning (MTL) to data harmonization
(DH); where we aim to harmonize images across different acquisition platforms
and sites. This allows us to integrate information from multiple acquisitions
and improve the predictive performance and learning efficiency of the
harmonization model. Specifically, we introduce the Multi Stage Prediction
(MSP) Network, a MTL framework that incorporates neural networks of potentially
disparate architectures, trained for different individual acquisition
platforms, into a larger architecture that is refined in unison. The MSP
utilizes high-level features of single networks for individual tasks, as inputs
of additional neural networks to inform the final prediction, therefore
exploiting redundancy across tasks to make the most of limited training data.
We validate our methods on a dMRI harmonization challenge dataset, where we
predict three modern platform types, from one obtained from an old scanner. We
show how MTL architectures, such as the MSP, produce around 20\% improvement of
patch-based mean-squared error over current state-of-the-art methods and that
our MSP outperforms off-the-shelf MTL networks. Our code is available
this https URL .

本文介绍了将多任务学习（MTL）引入数据协调（DH）的研究。我们提出了多阶段预测（MSP）网络，这是一个 MTL 框架，将神经网络整合到一个较大的结构中，以改善预测性能和学习效率。我们在一个 dMRI 协调挑战数据集上验证了我们的方法，并展示了 MSP 的优异性能。