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In recent years, several works have adopted the convolutional neural network(CNN) to diagnose the avascular necrosis of the femoral head (AVNFH) based onX-ray images or magnetic resonance imaging (MRI). However, due to the tissueoverlap, X-ray images are difficult to provide fine-grained features for earlydiagnosis. MRI, on the other hand, has a long imaging time, is more expensive,making it impractical in mass screening. Computed tomography (CT) showslayer-wise tissues, is faster to image, and is less costly than MRI. However,to our knowledge, there is no work on CT-based automated diagnosis of AVNFH. Inthis work, we collected and labeled a large-scale dataset for AVNFH ranking. Inaddition, existing end-to-end CNNs only yields the classification result andare difficult to provide more information for doctors in diagnosis. To addressthis issue, we propose the structure regularized attentive network (SRANet),which is able to highlight the necrotic regions during classification based onpatch attention. SRANet extracts features in chunks of images, obtains weightvia the attention mechanism to aggregate the features, and constrains them by astructural regularizer with prior knowledge to improve the generalization.SRANet was evaluated on our AVNFH-CT dataset. Experimental results show thatSRANet is superior to CNNs for AVNFH classification, moreover, it can localizelesions and provide more information to assist doctors in diagnosis. Our codesare made public at https://github.com/tomas-lilingfeng/SRANet.

Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization