PSScreen：部分监督下的多种视网膜疾病筛查
PSScreen: Partially Supervised Multiple Retinal Disease Screening
AI总结：
针对多站点部分标记视网膜疾病数据的域偏移与标签缺失问题，提出含双流结构的PSScreen模型，通过文本引导解耦对齐特征、伪标签一致性及自蒸馏提升性能，在域内外数据集上达SOTA。
摘要：
利用多个部分标记数据集训练多种视网膜疾病筛查模型可降低对全标注数据集的依赖，但由于不同医疗站点的训练数据集间存在显著域偏移，且存在部分类别标签缺失的问题，该任务仍具挑战性。为解决这些难题，我们提出PSScreen，一种新型的部分监督多种视网膜疾病筛查模型。PSScreen包含两条流：一条学习确定性特征，另一条通过不确定性注入学习概率性特征。随后，我们利用文本引导将两类特征解耦为疾病维度特征，并通过特征蒸馏对齐以提升域泛化能力。同时，我们采用两条流之间的伪标签一致性解决标签缺失问题，并引入自蒸馏将已知类别的任务相关语义从确定性流迁移到概率性流，以进一步提升检测性能。实验表明，PSScreen平均显著提升了六种视网膜疾病及正常状态的检测性能，在域内和域外数据集上均达到了当前最优结果。代码可在https://github.com/boyiZheng99/PSScreen获取。
英文摘要：
Leveraging multiple partially labeled datasets to train a model for multiple retinal disease screening reduces the reliance on fully annotated datasets, but remains challenging due to significant domain shifts across training datasets from various medical sites, and the label absent issue for partial classes. To solve these challenges, we propose PSScreen, a novel Partially Supervised multiple retinal disease Screening model. Our PSScreen consists of two streams and one learns deterministic features and the other learns probabilistic features via uncertainty injection. Then, we leverage the textual guidance to decouple two types of features into disease-wise features and align them via feature distillation to boost the domain generalization ability. Meanwhile, we employ pseudo label consistency between two streams to address the label absent issue and introduce a self-distillation to transfer task-relevant semantics about known classes from the deterministic to the probabilistic stream to further enhance the detection performances. Experiments show that our PSScreen significantly enhances the detection performances on six retinal diseases and the normal state averagely and achieves state-of-the-art results on both in-domain and out-of-domain datasets. Codes are available at https://github.com/boyiZheng99/PSScreen.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
