论文标题
弱监督的学习显着减少了头部CT上颅内出血所需的标签数量
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
论文作者
论文摘要
现代的机器学习管道，尤其是基于深度学习模型的管道，需要大量标记的数据。对于分类问题，最常见的学习范式包括在培训过程中介绍标记的示例，从而对构成正面和负面样本的强大监督。这构成了放射学中DL模型开发的主要障碍 - 特别是横截面成像（例如，计算机断层扫描[CT]扫描） - 标签必须来自图像或切片级别的专家放射线专家的手动注释。这些与考试级别的注释不同，这些注释更粗糙但更便宜，可以使用自然语言处理技术从放射学报告中提取。这项工作研究了脑CT中颅内出血检测问题应收集哪种标签的问题。我们研究图像级注释是否应优于考试级别的注释。通过将这项任务作为多个实例学习问题，并采用现代化的基于注意力的DL体系结构，我们分析了不同级别的监督级别改善检测性能的程度。 We find that strong supervision (i.e., learning with local image-level annotations) and weak supervision (i.e., learning with only global examination-level labels) achieve comparable performance in examination-level hemorrhage detection (the task of selecting the images in an examination that show signs of hemorrhage) as well as in image-level hemorrhage detection (highlighting those signs within the selected images).此外，我们研究这种行为是训练过程中可用标签数量的函数。我们的结果表明，对于这些任务，本地标签可能根本不需要，从而大大减少了收集和策划数据集涉及的时间和成本。
Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing strong supervision on what constitutes positive and negative samples. This constitutes a major obstacle for the development of DL models in radiology--in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)--where labels must come from manual annotations by expert radiologists at the image or slice-level. These differ from examination-level annotations, which are coarser but cheaper, and could be extracted from radiology reports using natural language processing techniques. This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a multiple instance learning problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve detection performance. We find that strong supervision (i.e., learning with local image-level annotations) and weak supervision (i.e., learning with only global examination-level labels) achieve comparable performance in examination-level hemorrhage detection (the task of selecting the images in an examination that show signs of hemorrhage) as well as in image-level hemorrhage detection (highlighting those signs within the selected images). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all for these tasks, drastically reducing the time and cost involved in collecting and curating datasets.
