Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan.
Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan; Center for Cyber-Physical Systems (C2PS), Department of Electrical Engineering and Computer Sciences, Khalifa University, Abu Dhabi, 127788, United Arab Emirates.
Comput Biol Med. 2021 Jul;134:104435. doi: 10.1016/j.compbiomed.2021.104435. Epub 2021 May 8.
The human respiratory network is a vital system that provides oxygen supply and nourishment to the whole body. Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized deep learning systems (in both transfer learning and fine-tuning modes) to diagnose pulmonary disorders using chest X-rays (CXRs). However, such systems require exhaustive training efforts on large-scale (and well-annotated) data to effectively diagnose chest abnormalities (at the inference stage). Furthermore, procuring such large-scale data (in a clinical setting) is often infeasible and impractical, especially for rare diseases. With the recent advances in incremental learning, researchers have periodically tuned deep neural networks to learn different classification tasks with few training examples. Although, such systems can resist catastrophic forgetting, they treat the knowledge representations (which the network learns periodically) independently of each other, and this limits their classification performance. Also, to the best of our knowledge, there is no incremental learning-driven image diagnostic framework (to date) that is specifically designed to screen pulmonary disorders from the CXRs. To address this, we present a novel framework that can learn to screen different chest abnormalities incrementally (via few-shot training). In addition to this, the proposed framework is penalized through an incremental learning loss function that infers Bayesian theory to recognize structural and semantic inter-dependencies between incrementally learned knowledge representations to diagnose the pulmonary diseases effectively (at the inference stage), regardless of the scanner specifications. We tested the proposed framework on five public CXR datasets containing different chest abnormalities, where it achieved an accuracy of 0.8405 and the F1 score of 0.8303, outperforming various state-of-the-art incremental learning schemes. It also achieved a highly competitive performance compared to the conventional fine-tuning (transfer learning) approaches while significantly reducing the training and computational requirements.
人体呼吸系统是一个至关重要的系统，为全身提供氧气供应和营养。肺部疾病会导致严重的呼吸问题，如果不及时治疗，可能导致突然死亡。许多研究人员利用深度学习系统（在迁移学习和微调模式下）使用胸部 X 光片（CXR）诊断肺部疾病。然而，这些系统需要在大规模（和标记良好）数据上进行详尽的训练工作，以便在推断阶段有效地诊断胸部异常。此外，在临床环境中获取如此大规模的数据通常是不可行和不切实际的，尤其是对于罕见疾病。随着增量学习的最新进展，研究人员定期调整深度神经网络，以便用少量训练示例学习不同的分类任务。尽管如此，这些系统可以抵抗灾难性遗忘，但它们彼此独立地对待网络周期性学习的知识表示，这限制了它们的分类性能。此外，据我们所知，迄今为止，还没有基于增量学习的图像诊断框架（专门用于从 CXR 中筛选肺部疾病）。为了解决这个问题，我们提出了一个新颖的框架，可以通过少量样本训练来学习增量式地筛选不同的胸部异常。除此之外，所提出的框架通过增量学习损失函数进行惩罚，该函数推断贝叶斯理论，以识别增量学习的知识表示之间的结构和语义相互依赖性，从而在推断阶段有效地诊断肺部疾病，而不受扫描仪规格的影响。我们在包含不同胸部异常的五个公共 CXR 数据集上测试了所提出的框架，它在那里实现了 0.8405 的准确率和 0.8303 的 F1 分数，优于各种最新的增量学习方案。与传统的微调（迁移学习）方法相比，它也取得了极具竞争力的性能，同时大大减少了训练和计算需求。
