Medical image analysis using computer-based algorithms has attracted
considerable attention from the research community and achieved tremendous
progress in the last decade. With recent advances in computing resources and
availability of large-scale medical image datasets, many deep learning models
have been developed for disease diagnosis from medical images. However,
existing techniques focus on sub-tasks, e.g., disease classification and
identification, individually, while there is a lack of a unified framework
enabling multi-task diagnosis. Inspired by the capability of Vision
Transformers in both local and global representation learning, we propose in
this paper a new method, namely Multi-task Vision Transformer (MVC) for
simultaneously classifying chest X-ray images and identifying affected regions
from the input data. Our method is built upon the Vision Transformer but
extends its learning capability in a multi-task setting. We evaluated our
proposed method and compared it with existing baselines on a benchmark dataset
of COVID-19 chest X-ray images. Experimental results verified the superiority
of the proposed method over the baselines on both the image classification and
affected region identification tasks.

使用计算机算法进行医学图像分析在过去的十年中引起了研究界的广泛关注并取得了巨大进展。本文提出了一种新方法，即多任务视觉 Transformer（MVC），用于同时对胸部 X 射线图像进行分类和识别受影响的区域，实验证实了该方法在图像分类和受影响区域识别任务上的优越性。

MVC: 一种用于从胸部 X 光图像进行 COVID-19 诊断的多任务视觉变压器网络

MVC: A Multi-Task Vision Transformer Network for COVID-19 Diagnosis from  Chest X-ray Images

Intelligent fault diagnosis is essential to safe operation of machinery.
However, due to scarce fault samples and data heterogeneity in field machinery,
deep learning based diagnosis methods are prone to over-fitting with poor
generalization ability. To solve the problem, this paper proposes a
personalized federated learning framework, enabling multi-task fault diagnosis
method across multiple factories in a privacypreserving manner. Firstly,
rotating machines from different factories with similar vibration feature data
are categorized into machine groups using a federated clustering method. Then,
a multi-task deep learning model based on convolutional neural network is
constructed to diagnose the multiple faults of machinery with heterogeneous
information fusion. Finally, a personalized federated learning framework is
proposed to solve data heterogeneity across different machines using adaptive
hierarchical aggregation strategy. The case study on collected data from real
machines verifies the effectiveness of the proposed framework. The result shows
that the diagnosis accuracy could be improved significantly using the proposed
personalized federated learning, especially for those machines with scarce
fault samples.

本研究提出一种个性化联邦学习框架，使用联邦聚类方法将具有相似振动特征数据的不同工厂旋转机器进行分类，并通过多任务深度学习模型进行异构信息融合进行多任务故障诊断，最终采用自适应分级汇聚策略解决不同机器之间的数据异构性问题。案例研究验证了所提出的框架的有效性，特别对于那些故障样本匮乏的机器，使用所提出的个性化联邦学习可以显著提高诊断精度。