Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment

TL;DR

This paper introduces Deep Interactive Learning (DIaL), a method that significantly reduces annotation time for training CNNs to assess osteosarcoma treatment response, achieving accurate necrosis ratio estimation with minimal labeling effort.

Contribution

The paper presents DIaL, an efficient interactive annotation approach that minimizes manual labeling time for training CNNs in medical image segmentation tasks.

Findings

CNN trained with 7 hours of annotation achieves expert-level necrosis ratio estimation.

DIaL reduces annotation effort compared to traditional supervised learning methods.

Model predictions fall within inter-observer variability for pathology assessments.

Abstract

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.