论文标题
减少肺结节诊断自替代模型的注释需求
Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis
论文作者
论文摘要
基于特征的自我解释方法解释了他们的分类，以人为理解的特征。在医学成像社区中，这种临床知识的语义匹配大大增加了AI的可信度。但是，功能附加注释的成本仍然是一个紧迫的问题。我们通过提出CredAnno来解决这个问题，这是一种用于肺结核诊断的数据/注释有效的自我解释方法。 Credanno通过引入自我保护的对比学习来大大减少注释需求，以减轻从注释中学习大多数参数的负担，从而通过两阶段的培训代替端到端的培训。当使用数百个结节样本训练和仅1％的注释时，Credanno在预测恶性肿瘤方面取得了竞争力的准确性，同时，在预测结节属性方面，大多数以前的作品都显着超过了。学习空间的可视化进一步表明，恶性肿瘤和结节属性之间的相关性与临床知识一致。我们的完整代码可用：https：//github.com/diku-dk/credanno。
Feature-based self-explanatory methods explain their classification in terms of human-understandable features. In the medical imaging community, this semantic matching of clinical knowledge adds significantly to the trustworthiness of the AI. However, the cost of additional annotation of features remains a pressing issue. We address this problem by proposing cRedAnno, a data-/annotation-efficient self-explanatory approach for lung nodule diagnosis. cRedAnno considerably reduces the annotation need by introducing self-supervised contrastive learning to alleviate the burden of learning most parameters from annotation, replacing end-to-end training with two-stage training. When training with hundreds of nodule samples and only 1% of their annotations, cRedAnno achieves competitive accuracy in predicting malignancy, meanwhile significantly surpassing most previous works in predicting nodule attributes. Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge. Our complete code is open-source available: https://github.com/diku-dk/credanno.
