Abstract
The limited availability of annotated data presents a major challenge for applying deep learning methods to medical image analysis. Few-shot learning methods aim to recognize new classes from only a small number of labeled examples. These methods are typically studied under the standard few-shot learning setting, where all classes in a task are new. However, medical applications such as pathology classification from chest X-rays often require learning new classes while simultaneously leveraging knowledge of previously known ones, a scenario more closely aligned with generalized few-shot classification. Despite its practical relevance, few-shot learning has been scarcely studied in this context. In this work, we present MetaChest, a large-scale dataset of 479,215 chest X-rays collected from four public databases. MetaChest includes a meta-set partition specifically designed for standard few-shot classification, as well as an algorithm for generating multi-label episodes. We conduct extensive experiments evaluating both a standard transfer learning approach and an extension of ProtoNet across a wide range of few-shot multi-label classification tasks. Our results demonstrate that increasing the number of classes per episode and the number of training examples per class improves classification performance. Notably, the transfer learning approach consistently outperforms the ProtoNet extension, despite not being tailored for few-shot learning. We also show that higher-resolution images improve accuracy at the cost of additional computation, while efficient model architectures achieve comparable performance to larger models with significantly reduced resource requirements.
Abstract (translated)
标注数据的有限可用性为将深度学习方法应用于医学图像分析带来了重大挑战。少数样本学习（few-shot learning）方法旨在仅从少量有标签的例子中识别新的类别。这些方法通常在标准少数样本学习设置下进行研究，其中任务中的所有类都是全新的。然而，在诸如胸部X光片病理分类的医疗应用中，往往需要同时学习新类和利用已知类的知识，这种情况更符合泛化少数样本分类（generalized few-shot classification）的情景。尽管其实用性很强，但在这个背景下很少有对少数样本学习的研究。 在本工作中，我们介绍了MetaChest，这是一个大规模的数据集，包含来自四个公开数据库的479,215张胸部X光片图像。MetaChest包括一个专门为标准少数样本分类设计的元数据集分割，并且包含用于生成多标签序列的算法。我们进行了广泛的实验，评估了一种传统的迁移学习方法和ProtoNet（一种原型网络）的扩展在一系列广泛的少数样本多标签分类任务中的表现。我们的结果表明，在每个序列中增加类的数量以及每种类别训练示例的数量可以提高分类性能。 值得注意的是，尽管不是为少数样本学习量身定制，但转移学习的方法始终优于ProtoNet的扩展方法。我们还展示了高分辨率图像在牺牲更多计算资源的情况下提高了准确性，而高效的模型架构则实现了与更大模型相当的表现力，同时显著减少了资源需求。
URL
https://arxiv.org/abs/2509.25590
