Dynamic Triple Reweighting Network for Automatic Femoral Head Necrosis Diagnosis from Computed Tomography

Avascular necrosis of the femoral head (AVNFH) is a common orthopedic disease that seriously affects the life quality of middle-aged and elderly people. Early AVNFH is difficult to diagnose due to its complex symptoms. In recent years, some works have applied deep learning algorithms to find traces of early AVNFH in X-rays or magnetic resonance imaging (MRI). However, X-rays are difficult to reflect hidden features due to the tissue overlap; MRI is sensitive but requires more time for imaging and is expensive. This study aims to develop a computer-aided diagnosis system for early AVNFH based on computed tomography (CT), which provides layer-wise features and is less costly. To achieve this, a large-scale dataset for AVNFH was collected and annotated by experienced doctors. We propose the Dynamic Triple Reweighting Network (DTRNet) that integrates the AVNFH classification and weakly-supervised localization. DTRNet incorporates nested multi-instance learning as the first and second reweighting, and structure regularization as the third reweighting to identify diseases and localize the lesion region. Since nested multi-instance learning is inapplicable in situations with few positive samples in the patch set, we propose a dynamic pseudo-package module to compensate for this limitation. Experimental results show that DTRNet is superior to the baselines in AVNFH classification. In addition, it can locate lesions to provide more information for assisting clinical decisions. The desensitized data and codes has been made available at: https://github.com/tomas-lilingfeng/DTRNet.