上海科技大学知识管理系统

Cervical cancer is a significant health burden worldwide, and computer-aided diagnosis (CAD) pipelines have the potential to improve diagnosis efficiency and treatment outcomes. However, traditional CAD pipelines have limitations due to the requirement of a detection model trained on a large annotated dataset, which can be expensive and time-consuming. They also have a clear performance limit and low data utilization efficiency. To address these issues, we introduce a two-stage detection-free pipeline, incorporating pooling transformer and MoCo pretraining strategies, that optimizes data utilization for whole slide images (WSIs) while relying solely on sample-level diagnosis labels for training. The experimental results demonstrate the effectiveness of our approach, with performance scaling up as the amount of data increases. Overall, our novel pipeline has the potential to fully utilize massive data in WSI classification and can significantly improve cancer diagnosis and treatment. By reducing the reliance on expensive data labeling and detection models, our approach could enable more widespread and cost-effective implementation of CAD pipelines in clinical settings. Our code and model is available at https://github.com/thebestannie/Detection-free-MICCAI2023. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.