Medical image classification plays a crucial role in computer-aided clinical
diagnosis. While deep learning techniques have significantly enhanced
efficiency and reduced costs, the privacy-sensitive nature of medical imaging
data complicates centralized storage and model training. Furthermore,
low-resource healthcare organizations face challenges related to communication
overhead and efficiency due to increasing data and model scales. This paper
proposes a novel privacy-preserving medical image classification framework
based on federated learning to address these issues, named FedMIC. The
framework enables healthcare organizations to learn from both global and local
knowledge, enhancing local representation of private data despite statistical
heterogeneity. It provides customized models for organizations with diverse
data distributions while minimizing communication overhead and improving
efficiency without compromising performance. Our FedMIC enhances robustness and
practical applicability under resource-constrained conditions. We demonstrate
FedMIC's effectiveness using four public medical image datasets for classical
medical image classification tasks.

本论文提出了一种基于联邦学习的隐私保护医学图像分类框架 (FedMIC)，通过全局和局部知识使医疗组织从私有数据中获得定制模型，同时减少通信开销和提高效率，提高在资源受限条件下的稳健性和实际适用性。通过使用四个公共医学图像数据集展示了 FedMIC 的有效性。

医学图像分类的联邦蒸馏：走向可靠的计算机辅助诊断

Federated Distillation for Medical Image Classification: Towards  Trustworthy Computer-Aided Diagnosis

This study introduces a novel expert generation method that dynamically
reduces task and computational complexity without compromising predictive
performance. It is based on a new hierarchical classification network topology
that combines sequential processing of generic low-level features with
parallelism and nesting of high-level features. This structure allows for the
innovative extraction technique: the ability to select only high-level features
of task-relevant categories. In certain cases, it is possible to skip almost
all unneeded high-level features, which can significantly reduce the inference
cost and is highly beneficial in resource-constrained conditions. We believe
this method paves the way for future network designs that are lightweight and
adaptable, making them suitable for a wide range of applications, from compact
edge devices to large-scale clouds. In terms of dynamic inference our
methodology can achieve an exclusion of up to 88.7\,\% of parameters and
73.4\,\% fewer giga-multiply accumulate (GMAC) operations, analysis against
comparative baselines showing an average reduction of 47.6\,\% in parameters
and 5.8\,\% in GMACs across the cases we evaluated.

该研究引入了一种新的专家生成方法，它通过将顺序处理通用低级特征与高级特征的并行和嵌套结合起来的新的分层分类网络拓扑结构，实现了动态降低任务和计算复杂度的能力。该方法在资源受限条件下能够选择只有任务相关类别的高级特征，可以显著减少推理成本，对于轻量级和可适应性的网络设计具有前瞻性意义。在动态推理方面，我们的方法可以排除高达 88.7% 的参数和 73.4% 的 GMAC 操作，与比较基准相比，在所评估的案例中，平均减少了 47.6% 的参数和 5.8% 的 GMAC 操作。

选择高层特征：来自层次分类网络的高效专家

Select High-Level Features: Efficient Experts from a Hierarchical  Classification Network

Digital media is ubiquitous and produced in ever-growing quantities. This
necessitates a constant evolution of compression techniques, especially for
video, in order to maintain efficient storage and transmission. In this work,
we aim at exploiting non-local redundancies in video data that remain difficult
to erase for conventional video codecs. We design convolutional neural networks
with a particular emphasis on low memory and computational footprint. The
parameters of those networks are trained on the fly, at encoding time, to
predict the residual signal from the decoded video signal. After the training
process has converged, the parameters are compressed and signalled as part of
the code of the underlying video codec. The method can be applied to any
existing video codec to increase coding gains while its low computational
footprint allows for an application under resource-constrained conditions.
Building on top of High Efficiency Video Coding, we achieve coding gains
similar to those of pretrained denoising CNNs while only requiring about 1% of
their computational complexity. Through extensive experiments, we provide
insights into the effectiveness of our network design decisions. In addition,
we demonstrate that our algorithm delivers stable performance under conditions
met in practical video compression: our algorithm performs without significant
performance loss on very long random access segments (up to 256 frames) and
with moderate performance drops can even be applied to single frames in
high-resolution low delay settings.

该研究旨在利用卷积神经网络及非局部冗余优化视频压缩，达到与预处理去噪 CNN 类似的编码增益，同时只需要约为 1% 的计算复杂度，适用于资源受限的条件下进行视频压缩。