Bone tumor necrosis rate detection in few-shot X-rays based on deep learning.

Abstract

Although biopsy-based necrosis rate is a golden standard for reflecting the sensitivity of bone tumor and guiding postoperative chemotherapy, it requires biopsy which is invasive and time-consuming. In this paper, we develop a new necrosis rate detection method using time series X-ray images instead of biopsy. To overcome the limitations of few-shot samples, the proposed method utilizes a Generative Adversarial Network with Long Short-term Memory to generate time series X-ray images. For further data expansion, an image-to-image translation network is applied for producing the initial images. These augmented data are treated as the training set of a 3D-Convolutional Neural Network classification model. Our method expands the few-shot bone tumor X-rays by 10 times, and approaches the necrotic rate classification result of biopsy, which is the state-of-the-art technique in the detection of few-shot bone tumor necrosis rate. Furthermore, it provides an efficient method to investigate the bone tumor necrosis rate in few-shot samples.