==================CLASSIFICATION===============================

[1]        Li, Qingfeng, et al. "Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Prediction of MCI Progression." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[2]        Guo, Xiaoqing, and Yixuan Yuan. "Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classification." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[3]        Kazi, Anees, et al. "Graph Convolution Based Attention Model for Personalized Disease Prediction." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[4]        Lian, Chunfeng, et al. "End-to-End Dementia Status Prediction from Brain MRI Using Multi-task Weakly-Supervised Attention Network." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

 ==================SEGMENTATION================================

[5]        Zhang, Zhijie, et al. "ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[6]        Mou, Lei, et al. "CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[7]        Zhang, Shihao, et al. "Attention Guided Network for Retinal Image Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[8]        Wang, Guotai, et al. "Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[9]        Wu, Kai, et al. "Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[10]     Yuan, Wenguang, et al. "Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation from Multimodal Unpaired Images." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[11]     Huang, Qiaoying, et al. "Brain Segmentation from k-Space with End-to-End Recurrent Attention Network." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[12]     Zhang, Hang, et al. "RSANet: Recurrent Slice-Wise Attention Network for Multiple Sclerosis Lesion Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[13]     Xu, Hai, et al. "Deep Cascaded Attention Network for Multi-task Brain Tumor Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[14]     Chen, Shuai, et al. "Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

 =================DETECTION===================================

[15]     Wang, Xudong, et al. "Volumetric Attention for 3D Medical Image Segmentation and Detection." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[16]     Tao, Qingyi, et al. "Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[17]     Li, Zihao, et al. "MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[18]     Zhong, Zhusi, et al. "An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.

[19]     Ma, Congbo, Hu Wang, and Steven CH Hoi. "Multi-label Thoracic Disease Image Classification with Cross-Attention Networks." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.