深度交互式学习：一种基于深度学习的骨肉瘤治疗反应评估的有效标记方法
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
摘要：
骨肉瘤是最常见的恶性原发性骨肿瘤。标准治疗包括术前化疗和手术切除。通过坏死肿瘤面积与总肿瘤面积的比值测量的治疗反应是总生存期的已知预后因素。该评估目前由病理学家通过在显微镜下观察载玻片手动完成，由于其主观性质，可能无法重现。卷积神经网络（CNN）可用于自动分割骨肉瘤全切片图像上的存活和坏死肿瘤。监督学习的一个瓶颈是训练需要大量准确的注释，这是一个耗时且昂贵的过程。在本文中，我们将深度交互式学习（DIaL）描述为训练CNN的有效标记方法。在完成初始标记步骤之后，注释器只需要从先前的分割预测中校正错误标记的区域，以改进CNN模型，直到实现令人满意的预测。我们的实验表明，我们的CNN模型通过使用DIaL仅7小时的注释训练，可以成功地估计非标准化手动手术病理学任务的预期观察者间变化率内的坏死比率。
英文摘要：
Osteosarcoma is the most common malignant primary bone tumor. Standard treatment includes pre-operative chemotherapy followed by surgical resection. The response to treatment as measured by ratio of necrotic tumor area to overall tumor area is a known prognostic factor for overall survival. This assessment is currently done manually by pathologists by looking at glass slides under the microscope which may not be reproducible due to its subjective nature. Convolutional neural networks (CNNs) can be used for automated segmentation of viable and necrotic tumor on osteosarcoma whole slide images. One bottleneck for supervised learning is that large amounts of accurate annotations are required for training which is a time-consuming and expensive process. In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs. After an initial labeling step is done, annotators only need to correct mislabeled regions from previous segmentation predictions to improve the CNN model until the satisfactory predictions are achieved. Our experiments show that our CNN model trained by only 7 hours of annotation using DIaL can successfully estimate ratios of necrosis within expected inter-observer variation rate for non-standardized manual surgical pathology task.
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