Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
This work addresses the bottleneck of expensive and time-consuming annotations for pathologists in osteosarcoma diagnosis, though it is incremental as it builds on existing CNN methods with a novel labeling strategy.
The paper tackles the problem of time-consuming manual annotation for training convolutional neural networks (CNNs) in osteosarcoma treatment response assessment by proposing Deep Interactive Learning (DIaL), an efficient labeling approach where annotators correct mislabeled regions iteratively, resulting in a model trained with only 7 hours of annotation that estimates necrosis ratios within expected inter-observer variation rates.
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.