The shift towards efficient and automated data analysis through Machine
Learning (ML) has notably impacted healthcare systems, particularly Radiomics.
Radiomics leverages ML to analyze medical images accurately and efficiently for
precision medicine. Current methods rely on Deep Learning (DL) to improve
performance and accuracy (Deep Radiomics). Given the sensitivity of medical
images, ensuring privacy throughout the Deep Radiomics pipeline-from data
generation and collection to model training and inference-is essential,
especially when outsourced. Thus, Privacy-Enhancing Technologies (PETs) are
crucial tools for Deep Radiomics. Previous studies and systematization efforts
have either broadly overviewed PETs and their applications or mainly focused on
subsets of PETs for ML algorithms. In Deep Radiomics, where efficiency,
accuracy, and privacy are crucial, many PETs, while theoretically applicable,
may not be practical without specialized optimizations or hybrid designs.
Additionally, not all DL models are suitable for Radiomics. Consequently, there
is a need for specialized studies that investigate and systematize the
effective and practical integration of PETs into the Deep Radiomics pipeline.
This work addresses this research gap by (1) classifying existing PETs,
presenting practical hybrid PETS constructions, and a taxonomy illustrating
their potential integration with the Deep Radiomics pipeline, with comparative
analyses detailing assumptions, architectural suitability, and security, (2)
Offering technical insights, describing potential challenges and means of
combining PETs into the Deep Radiomics pipeline, including integration
strategies, subtilities, and potential challenges, (3) Proposing potential
research directions, identifying challenges, and suggesting solutions to
enhance the PETs in Deep Radiomics.

通过对我们所谓的深度放射学管道中 PETs 的实际应用进行系统研究，本文旨在填补现有研究的空白，提出了一种对现有 PETs 进行分类的方法，提供了实用的混合 PETs 构造，并通过比较分析了其在深度放射学管道中的潜在集成方式、假设、适用性和安全性，并提出了一系列技术见解、潜在挑战以及结合 PETs 到深度放射学管道的方法和策略，并探讨了未来的研究方向、挑战以及增强 PETs 在深度放射学中的可能解决方案。