Weakly supervised learning with noisy data has drawn attention in the medical
imaging community due to the sparsity of high-quality disease labels. However,
little is known about the limitations of such weakly supervised learning and
the effect of these constraints on disease classification performance. In this
paper, we test the effects of such weak supervision by examining model
tolerance for three conditions. First, we examined model tolerance for noisy
data by incrementally increasing error in the labels within the training data.
Second, we assessed the impact of dataset size by varying the amount of
training data. Third, we compared performance differences between binary and
multi-label classification. Results demonstrated that the model could endure up
to 10% added label error before experiencing a decline in disease
classification performance. Disease classification performance steadily rose as
the amount of training data was increased for all disease classes, before
experiencing a plateau in performance at 75% of training data. Last, the binary
model outperformed the multilabel model in every disease category. However,
such interpretations may be misleading, as the binary model was heavily
influenced by co-occurring diseases and may not have learned the specific
features of the disease in the image. In conclusion, this study may help the
medical imaging community understand the benefits and risks of weak supervision
with noisy labels. Such studies demonstrate the need to build diverse,
large-scale datasets and to develop explainable and responsible AI.

通过分析弱监督学习在医学图像领域的限制以及约束条件对疾病分类性能的影响，本研究揭示了模型对噪声数据的容忍度、训练数据量的影响以及二元和多标签分类之间的性能差异。结果表明，模型可以在添加 10% 标签错误之前保持疾病分类的性能，随着训练数据量的增加，各种疾病类别的分类性能稳步提升，但在 75% 训练数据量时性能趋于平稳，同时二元模型在每个疾病类别上优于多标签模型，然而这些解释可能具有误导性，因为二元模型受到共同出现的疾病的影响，并未学习到图像中疾病的具体特征。综上所述，本研究有助于医学图像领域理解弱监督学习和噪声标签的优势和风险，同时也表明了构建多样化的大规模数据集以及开发可解释和负责任的人工智能的需求。