Medical imaging analysis has witnessed remarkable advancements even
surpassing human-level performance in recent years, driven by the rapid
development of advanced deep-learning algorithms. However, when the inference
dataset slightly differs from what the model has seen during one-time training,
the model performance is greatly compromised. The situation requires restarting
the training process using both the old and the new data which is
computationally costly, does not align with the human learning process, and
imposes storage constraints and privacy concerns. Alternatively, continual
learning has emerged as a crucial approach for developing unified and
sustainable deep models to deal with new classes, tasks, and the drifting
nature of data in non-stationary environments for various application areas.
Continual learning techniques enable models to adapt and accumulate knowledge
over time, which is essential for maintaining performance on evolving datasets
and novel tasks. This systematic review paper provides a comprehensive overview
of the state-of-the-art in continual learning techniques applied to medical
imaging analysis. We present an extensive survey of existing research, covering
topics including catastrophic forgetting, data drifts, stability, and
plasticity requirements. Further, an in-depth discussion of key components of a
continual learning framework such as continual learning scenarios, techniques,
evaluation schemes, and metrics is provided. Continual learning techniques
encompass various categories, including rehearsal, regularization,
architectural, and hybrid strategies. We assess the popularity and
applicability of continual learning categories in various medical sub-fields
like radiology and histopathology...

在医学影像分析中，深度学习算法的快速发展使得医学影像分析取得了显著的进展，但是在推理数据集与模型训练时所见数据略有不同的情况下，模型的性能会受到很大影响。因此，需要采用连续学习技术来处理新类别、新任务和非稳态环境中的数据变化。本系统综述论文对连续学习技术在医学影像分析中的应用进行了全面的概述，包括灾难性遗忘、数据漂移、稳定性和可塑性要求等方面的研究，同时对连续学习方案、技术、评价方案和指标等关键组成部分进行了深入讨论。

医学影像分析中的持续学习：最新进展与未来前景的综述

Continual Learning in Medical Imaging Analysis: A Comprehensive Review  of Recent Advancements and Future Prospects

Encoding-decoding CNNs play a central role in data-driven noise reduction and
can be found within numerous deep-learning algorithms. However, the development
of these CNN architectures is often done in ad-hoc fashion and theoretical
underpinnings for important design choices is generally lacking. Up to this
moment there are different existing relevant works that strive to explain the
internal operation of these CNNs. Still, these ideas are either scattered
and/or may require significant expertise to be accessible for a bigger
audience. In order to open up this exciting field, this article builds
intuition on the theory of deep convolutional framelets and explains diverse ED
CNN architectures in a unified theoretical framework. By connecting basic
principles from signal processing to the field of deep learning, this
self-contained material offers significant guidance for designing robust and
efficient novel CNN architectures.

这篇文章通过将信号处理的基本原理与深度学习领域相连接，在统一的理论框架下，建立了对深度卷积框图的直观理解，解释了多样的编码 - 解码卷积神经网络架构，并提供了设计稳健高效的新颖卷积神经网络架构的重要指导。