Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays

• URL: http://arxiv.org/abs/2012.15359v2
• Date: Tue, 16 Feb 2021 00:48:04 GMT
• Title: Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays
• Authors: Yirui Wang, Kang Zheng, Chi-Tung Chang, Xiao-Yun Zhou, Zhilin Zheng, Lingyun Huang, Jing Xiao, Le Lu, Chien-Hung Liao, Shun Miao
• Abstract summary: Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models is emerging. Previous methods failed to account for the low disease prevalence in medical records and utilize the image-level diagnosis indicated from the medical records. We propose a new knowledge distillation method that effectively exploits large-scale image-level labels extracted from the medical records.
• Score: 20.878741070685468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohibitively high labor costs involved in large-scale medical image annotations. Despite the extensive attentions received on SSL, previous methods failed to 1) account for the low disease prevalence in medical records and 2) utilize the image-level diagnosis indicated from the medical records. Both issues are unique to SSL for CAD models. In this work, we propose a new knowledge distillation method that effectively exploits large-scale image-level labels extracted from the medical records, augmented with limited expert annotated region-level labels, to train a rib and clavicle fracture CAD model for chest X-ray (CXR). Our method leverages the teacher-student model paradigm and features a novel adaptive asymmetric label sharpening (AALS) algorithm to address the label imbalance problem that specially exists in medical domain. Our approach is extensively evaluated on all CXR (N = 65,845) from the trauma registry of anonymous hospital over a period of 9 years (2008-2016), on the most common rib and clavicle fractures. The experiment results demonstrate that our method achieves the state-of-the-art fracture detection performance, i.e., an area under receiver operating characteristic curve (AUROC) of 0.9318 and a free-response receiver operating characteristic (FROC) score of 0.8914 on the rib fractures, significantly outperforming previous approaches by an AUROC gap of 1.63% and an FROC improvement by 3.74%. Consistent performance gains are also observed for clavicle fracture detection.

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