基于随机判别子空间集成的胸部X光片少样本诊断
Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces
- Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对医学影像标注稀缺问题，提出一种基于随机判别子空间集成的少样本胸部X光诊断方法，通过新颖损失函数提升子空间判别性，计算效率高，在公开数据集上取得良好效果。
AI中文摘要：
由于医学领域标注数据的稀缺性，少样本学习可能对医学图像分析任务有用。我们设计了一种使用随机子空间集成的少样本学习方法，用于胸部X光片（CXR）的诊断。我们的设计计算效率高，比使用流行的截断奇异值分解（t-SVD）进行子空间分解的方法快约1.8倍。所提出的方法通过最小化一种新颖的损失函数进行训练，该损失函数有助于在判别子空间中创建训练数据的良好分离簇。因此，最小化损失函数最大化子空间之间的距离，使其具有判别性并有助于更好的分类。在大型公开可用的CXR数据集上的实验产生了有希望的结果。项目代码将在https://github.com/Few-shot-Learning-on-chest-x-ray/fsl_subspace提供。
英文摘要：
Due to the scarcity of annotated data in the medical domain, few-shot learning may be useful for medical image analysis tasks. We design a few-shot learning method using an ensemble of random subspaces for the diagnosis of chest x-rays (CXRs). Our design is computationally efficient and almost 1.8 times faster than method that uses the popular truncated singular value decomposition (t-SVD) for subspace decomposition. The proposed method is trained by minimizing a novel loss function that helps create well-separated clusters of training data in discriminative subspaces. As a result, minimizing the loss maximizes the distance between the subspaces, making them discriminative and assisting in better classification. Experiments on large-scale publicly available CXR datasets yield promising results. Code for the project will be available at https://github.com/Few-shot-Learning-on-chest-x-ray/fsl_subspace.
