Inevitable domain and task discrepancies in real-world scenarios can impair
the generalization performance of the pre-trained deep models for medical data.
Therefore, we audaciously propose that we should build a general-purpose
medical AI system that can be seamlessly adapted to downstream domains/tasks.
Since the domain/task adaption procedures usually involve additional labeling
work for the target data, designing a data-efficient adaption algorithm is
desired to save the cost of transferring the learned knowledge. Our recent work
found that vision-language models (VLMs) are efficient learners with
extraordinary cross-domain ability. Therefore, in this work, we further explore
the possibility of leveraging pre-trained VLMs as medical foundation models for
building general-purpose medical AI, where we thoroughly investigate three
machine-learning paradigms, i.e., domain/task-specialized learning, joint
learning, and continual learning, for training the VLMs and evaluate their
generalization performance on cross-domain and cross-task test sets. To
alleviate the catastrophic forgetting during sequential training, we employ
rehearsal learning and receive a sharp boost in terms of generalization
capability. In a nutshell, our empirical evidence suggests that continual
learning may be a practical and efficient learning paradigm for the medical
foundation model. And we hope researchers can use our empirical evidence as
basement to further explore the path toward medical foundation model.

本文通过探究机器学习中三种主流学习方法，即领域 / 任务特殊化学习，联合学习，以及连续学习，并运用 VLMs 这一良好的跨领域学习模型作为医学基础模型，得到了在不同领域 / 任务测试集中的十分出色的泛化性能，并提出连续学习可能是构建医学基础模型的一种切实可行和高效的学习范式。

朝向通用医疗 AI：持续学习医学基础模型

Towards General Purpose Medical AI: Continual Learning Medical Foundation Model

Traditional classification algorithms assume that training and test data come
from similar distributions. This assumption is violated in adversarial
settings, where malicious actors modify instances to evade detection. A number
of custom methods have been developed for both adversarial evasion attacks and
robust learning. We propose the first systematic and general-purpose retraining
framework which can: a) boost robustness of an \emph{arbitrary} learning
algorithm, in the face of b) a broader class of adversarial models than any
prior methods. We show that, under natural conditions, the retraining framework
minimizes an upper bound on optimal adversarial risk, and show how to extend
this result to account for approximations of evasion attacks. Extensive
experimental evaluation demonstrates that our retraining methods are nearly
indistinguishable from state-of-the-art algorithms for optimizing adversarial
risk, but are more general and far more scalable. The experiments also confirm
that without retraining, our adversarial framework dramatically reduces the
effectiveness of learning. In contrast, retraining significantly boosts
robustness to evasion attacks without significantly compromising overall
accuracy.

提出了第一个系统性的通用的重新训练框架，可以在对抗性攻击的情况下提高任意学习算法的鲁棒性，并且是先前方法中更广泛的对抗模型的一类；该框架能够最小化对抗风险的最优上界，同时扩展该结果以考虑逃避攻击的近似值，从而显著提高抵抗逃避攻击的鲁棒性，而不会显著影响整体准确性的实验评估也证实了这一点。