用主动学习选关键视频帧，用一半数据达到全量数据的手术分割效果
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation
- 用深度网络自动挑选最能提升性能的手术视频帧进行标注
- 仅用一半数据就达到0.4349 mIoU，接近全数据集的0.4374 mIoU
- 适合医疗图像标注成本高的场景，尤其适用于微创手术分割
医学图像标注成本高昂，限制了深度学习应用。本文将主动学习引入腹腔镜胆囊切除术视频帧选择，构建高质量且低成本的语义分割数据集。主动学习使深度神经网络（DNN）可参与数据构建流程：利用已有模型从新采集的数据中识别最具信息量的样本。随着新增标注数据加入训练，DNN性能与泛化能力持续提升。我们评估了多种信息度量方法，发现深度特征距离在本任务中表现最佳。实验表明，使用主动学习选出的一半数据训练的DNN，在关键解剖结构和手术器械上的平均交并比（mIoU）达0.4349，几乎与全数据集训练的0.4374持平。
原文摘要 · Abstract (English)
Labeling has always been expensive in the medical context, which has hindered related deep learning application. Our work introduces active learning in surgical video frame selection to construct a high-quality, affordable Laparoscopic Cholecystectomy dataset for semantic segmentation. Active learning allows the Deep Neural Networks (DNNs) learning pipeline to include the dataset construction workflow, which means DNNs trained by existing dataset will identify the most informative data from the newly collected data. At the same time, DNNs' performance and generalization ability improve over time when the newly selected and annotated data are included in the training data. We assessed different data informativeness measurements and found the deep features distances select the most informative data in this task. Our experiments show that with half of the data selected by active learning, the DNNs achieve almost the same performance with 0.4349 mean Intersection over Union (mIoU) compared to the same DNNs trained on the full dataset (0.4374 mIoU) on the critical anatomies and surgical instruments.
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