Resumen
As a crucial task for disease diagnosis, existing semi-supervised segmentation approaches process labeled and unlabeled data separately, ignoring the relationships between them, thereby limiting further performance improvements. In this work, we introduce a transformer-based multi-task framework that concurrently leverages both labeled and unlabeled volumes by encoding shared representation patterns. We first integrate transformers into YOLOv5 to enhance segmentation capabilities and adopt a multi-task approach spanning shadow region detection and boundary localization. Subsequently, we leverage the mean teacher model to simultaneously learn from labeled and unlabeled inputs alongside orthogonal view representations, enabling our approach to harness all available annotations. Our network can improve the learning ability and attain superior performance. Extensive experiments demonstrate that the transformer-powered architecture encodes robust inter-sample relationships, unlocking substantial performance gains by capturing shared information between labeled and unlabeled data. By treating both data types concurrently and encoding their shared patterns, our framework addresses the limitations of existing semi-supervised approaches, leading to improved segmentation accuracy and robustness.