用生成数据缓解医疗影像增量学习中的灾难性遗忘。
Mitigating Catastrophic Forgetting in the Incremental Learning of Medical Images
- 利用生成图像进行知识蒸馏，保留旧任务知识。
- 在PI-CAI等多数据集上实现更高准确率与更快收敛。
- 适合数据分散、无法存储大样本的医疗场景。
本文提出一种增量学习方法，用于提升深度学习模型在分析T2加权磁共振成像（T2w MRI）前列腺癌检测中的准确性和效率。研究基于来自多个医疗机构的人工智能与放射科数据，聚焦于使用PI-CAI数据集进行前列腺癌检测任务。采用知识蒸馏（KD）技术，通过过往任务生成的图像指导后续任务的模型训练，有效提升了模型性能并加快了收敛速度。为验证方法的通用性与鲁棒性，我们在PI-CAI数据集（涵盖OCT和PathMNIST等多模态医学影像）及基准持续学习数据集CIFAR-10上进行了评估。结果表明，在数据来自不同医疗机构且无法存储大规模原始数据的场景下，该方法能通过生成图像实现知识保留，是医学图像分析中增量学习的可行方案。
原文摘要 · Abstract (English)
This paper proposes an Incremental Learning (IL) approach to enhance the accuracy and efficiency of deep learning models in analyzing T2-weighted (T2w) MRI medical images prostate cancer detection using the PI-CAI dataset. We used multiple health centers' artificial intelligence and radiology data, focused on different tasks that looked at prostate cancer detection using MRI (PI-CAI). We utilized Knowledge Distillation (KD), as it employs generated images from past tasks to guide the training of models for subsequent tasks. The approach yielded improved performance and faster convergence of the models. To demonstrate the versatility and robustness of our approach, we evaluated it on the PI-CAI dataset, a diverse set of medical imaging modalities including OCT and PathMNIST, and the benchmark continual learning dataset CIFAR-10. Our results indicate that KD can be a promising technique for IL in medical image analysis in which data is sourced from individual health centers and the storage of large datasets is not feasible. By using generated images from prior tasks, our method enables the model to retain and apply previously acquired knowledge without direct access to the original data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。