CAMP：病理学中的连续自适应学习模型
CAMP: Continuous and Adaptive Learning Model in Pathology
AI总结：
针对传统计算病理学将各诊断任务作为独立分类问题处理导致的效率低、成本高问题，提出通用统一的CAMP框架，可在不遗忘旧知识的情况下低成本适配新任务，在22个数据集17项任务上实现SOTA性能，大幅降低计算与存储开销，推动病理分类变革。
摘要：
病理学中存在大量诊断任务。传统计算病理学将这些任务制定并作为独立的单个图像分类问题来处理，从而导致计算效率低下且成本高昂。为应对这些挑战，我们提出了一种通用、统一的普适性框架，名为病理学连续自适应学习模型（CAMP），用于病理图像分类。CAMP是一种生成式、高效的自适应分类模型，它能够利用病理学特定的先验知识，以最低的计算成本学习任务特定知识，同时不会遗忘已有任务的知识，从而持续适配任意分类任务。我们在22个数据集上对CAMP进行了评估，涵盖17项分类任务，包含1171526个图像块和11811张病理切片。CAMP在大量数据集和任务的图像块级与切片级层面均实现了当前最优的分类性能，与传统分类模型相比，最多可减少94%的计算时间和85%的存储空间。我们的研究结果表明，CAMP能够为病理图像分类带来根本性变革，为全数字化、计算机化的病理学实践铺平道路。
英文摘要：
There exist numerous diagnostic tasks in pathology. Conventional computational pathology formulates and tackles them as independent and individual image classification problems, thereby resulting in computational inefficiency and high costs. To address the challenges, we propose a generic, unified, and universal framework, called a continuous and adaptive learning model in pathology (CAMP), for pathology image classification. CAMP is a generative, efficient, and adaptive classification model that can continuously adapt to any classification task by leveraging pathology-specific prior knowledge and learning taskspecific knowledge with minimal computational cost and without forgetting the knowledge from the existing tasks. We evaluated CAMP on 22 datasets, including 1,171,526 patches and 11,811 pathology slides, across 17 classification tasks. CAMP achieves state-of-theart classification performance on a wide range of datasets and tasks at both patch- and slide-levels and reduces up to 94% of computation time and 85% of storage memory in comparison to the conventional classification models. Our results demonstrate that CAMP can offer a fundamental transformation in pathology image classification, paving the way for the fully digitized and computerized pathology practice.
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