Ph.D. student Yulei Qin's paper "Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks" (by Yulei Qin, Juan Wen, Hao Zheng, Xiaolin Huang, Jie Yang, Lingqian Wu, Ning Song, Yue-Min Zhu, and Guang -Zhong Yang) was accepted by IEEE Transactions on Medical Imaging, a top journal in the field of medical imaging.

      This paper presents a CNNs- based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. 

       Codes and models are available at http://www.pami.sjtu.edu.cn/News/56.