Data scarcity is a significant obstacle hindering the learning of powerful
machine learning models in critical healthcare applications. Data-sharing
mechanisms among multiple entities (e.g., hospitals) can accelerate model
training and yield more accurate predictions. Recently, approaches such as
Federated Learning (FL) and Split Learning (SL) have facilitated collaboration
without the need to exchange private data. In this work, we propose a framework
for medical imaging classification tasks called Federated Split learning of
Vision transformer with Block Sampling (FeSViBS). The FeSViBS framework builds
upon the existing federated split vision transformer and introduces a block
sampling module, which leverages intermediate features extracted by the Vision
Transformer (ViT) at the server. This is achieved by sampling features (patch
tokens) from an intermediate transformer block and distilling their information
content into a pseudo class token before passing them back to the client. These
pseudo class tokens serve as an effective feature augmentation strategy and
enhances the generalizability of the learned model. We demonstrate the utility
of our proposed method compared to other SL and FL approaches on three publicly
available medical imaging datasets: HAM1000, BloodMNIST, and Fed-ISIC2019,
under both IID and non-IID settings. Code:
this https URL

本文提出了一种基于 Federated Split 视觉变换器的块采样的医学图像分类任务的框架，该框架利用中间特征并将其信息内容蒸馏成伪类令牌，以增强模型的泛化能力。

FeSViBS: 带块采样的 Transformer 视觉联邦分散学习

FeSViBS: Federated Split Learning of Vision Transformer with Block  Sampling

Recently, we have witnessed great progress in the field of medical imaging
classification by adopting deep neural networks. However, the recent advanced
models still require accessing sufficiently large and representative datasets
for training, which is often unfeasible in clinically realistic environments.
When trained on limited datasets, the deep neural network is lack of
generalization capability, as the trained deep neural network on data within a
certain distribution (e.g. the data captured by a certain device vendor or
patient population) may not be able to generalize to the data with another
distribution.
In this paper, we introduce a simple but effective approach to improve the
generalization capability of deep neural networks in the field of medical
imaging classification. Motivated by the observation that the domain
variability of the medical images is to some extent compact, we propose to
learn a representative feature space through variational encoding with a novel
linear-dependency regularization term to capture the shareable information
among medical data collected from different domains. As a result, the trained
neural network is expected to equip with better generalization capability to
the "unseen" medical data. Experimental results on two challenging medical
imaging classification tasks indicate that our method can achieve better
cross-domain generalization capability compared with state-of-the-art
baselines.

本文提出了一种简单而有效的方法来提高医学成像分类中深度神经网络的泛化能力，该方法通过使用新的线性依赖正则化项进行变分编码学习代表性特征空间，以捕获从不同领域收集的医学数据之间的可共享信息。实验结果表明，与现有方法相比，我们的方法可以实现更好的跨领域泛化能力。

基于线性依赖正则化的医学影像分类领域泛化

Domain Generalization for Medical Imaging Classification with  Linear-Dependency Regularization

The use of semantic segmentation for masking and cropping input images has
proven to be a significant aid in medical imaging classification tasks by
decreasing the noise and variance of the training dataset. However,
implementing this approach with classical methods is challenging: the cost of
obtaining a dense segmentation is high, and the precise input area that is most
crucial to the classification task is difficult to determine a-priori. We
propose a novel joint-training deep reinforcement learning framework for image
augmentation. A segmentation network, weakly supervised with policy gradient
optimization, acts as an agent, and outputs masks as actions given samples as
states, with the goal of maximizing reward signals from the classification
network. In this way, the segmentation network learns to mask unimportant
imaging features. Our method, Adversarial Policy Gradient Augmentation (APGA),
shows promising results on Stanford's MURA dataset and on a hip fracture
classification task with an increase in global accuracy of up to 7.33% and
improved performance over baseline methods in 9/10 tasks evaluated. We discuss
the broad applicability of our joint training strategy to a variety of medical
imaging tasks.

提出了深度强化学习框架 Adversarial Policy Gradient Augmentation (APGA)，利用语义分割进行图像增强，实验表明该方法在医学图像分类任务中有较好的表现。