Dimitriadis Avtantil, Trivizakis Eleftherios, Papanikolaou Nikolaos, Tsiknakis Manolis, Marias Kostas
Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Greece.
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410, Heraklion, Greece.
Insights Imaging. 2022 Dec 12;13(1):188. doi: 10.1186/s13244-022-01315-3.
Contemporary deep learning-based decision systems are well-known for requiring high-volume datasets in order to produce generalized, reliable, and high-performing models. However, the collection of such datasets is challenging, requiring time-consuming processes involving also expert clinicians with limited time. In addition, data collection often raises ethical and legal issues and depends on costly and invasive procedures. Deep generative models such as generative adversarial networks and variational autoencoders can capture the underlying distribution of the examined data, allowing them to create new and unique instances of samples. This study aims to shed light on generative data augmentation techniques and corresponding best practices. Through in-depth investigation, we underline the limitations and potential methodology pitfalls from critical standpoint and aim to promote open science research by identifying publicly available open-source repositories and datasets.
当代基于深度学习的决策系统因需要大量数据集才能生成通用、可靠且高性能的模型而闻名。然而，收集此类数据集具有挑战性，需要耗时的过程，其中还涉及时间有限的临床专家。此外，数据收集往往会引发伦理和法律问题，且依赖于成本高昂且具有侵入性的程序。诸如生成对抗网络和变分自编码器等深度生成模型可以捕捉所检查数据的潜在分布，从而使它们能够创建新的、独特的样本实例。本研究旨在阐明生成式数据增强技术及相应的最佳实践。通过深入调查，我们从批判性的角度强调了局限性和潜在的方法陷阱，并旨在通过识别公开可用的开源存储库和数据集来促进开放科学研究。
