MIRACLE 2020年一季度季报

新闻动态：

• 祝贺周少华研究员受邀担任 MICCAI 2020 Program Chair !

MICCAI2020是MICCAI(Medical Image Computing and Computer Assisted Intervention) Society的年会，是医学影像计算和计算机辅助介入的顶级专业会议。今年10月在秘鲁的利马召开。

• 祝贺周少华研究员当选IEEE Fellow !

因在“医学影像分析和人脸识别”领域的贡献,周少华研究员当选IEEE Fellow，任期从2020年1月1日开始。

论文发表：

H. Li, H. Han, Z. Li, L. Wang, Z. Wu, J. Lu, and S. Kevin Zhou, High-Resolution Chest X-ray Bone Suppression Using Unpaired CT Structural Priors. IEEE Trans. on Medical Imaging, 2020. (Accepted)
本文提出了一种由粗到细的高分辩率X光胸片骨抑制模型，能利用非成对的3D CT影像中的结构化影像知识，实现对2D X光胸片中骨抑制，进而可以减少对肺部区域造成遮挡，提高医生对肺部疾病诊断的准确性

C. Peng, W.-A. Lin, H. Liao, R. Chellappa, S. Kevin Zhou, SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis. IEEE CVPR, 2020. (Accepted)
本文在业界首次提出针对体素精度自适应（跨精度）的医学影像插值算法，实现更精确的层间插值。

B. Zhou, S. Kevin Zhou, DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior. IEEE CVPR, 2020. (Accepted)
本文提出了MRI快速重建算法的新思路，首次系统地在双域上进行信息恢复以及利用递归设计来实现更精确的重建。无论采用不同的采样方式，重建精度都有很大提高；利用T1先验信息更进一步提高了重建精度。

文章选读：

2020.02.01: 姚青松 (Generative adversarial network )

Paper-1: M. Liu, T. Wang, J. Zhu: Semantic Image Synthesis with Spatially-Adaptive Normalization Taesung Park. IEEE CVPR, 2019.

Paper-2: M. Liu, Y. Ding, M. Xia, X. Liu, E. Ding, W. Zuo, S. Wen: STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing. IEEE ICCV, 2019.

Paper-3: P. Wu, Y. Lin, C. Chang, E. Y. Chang, S. Liao: RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes. IEEE ICCV, 2019.

Paper-3 (RelGAN):

2020.02.17: 朱玖闻 (Superpixel & Unsupervised representation learning)

Paper-1: R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk: SLIC Superpixels Compared to State-of-the-art Superpixel Methods. PAMI, 2012.

Paper-2: V. Jampani, D. Sun, M. Liu, M. Yang, J. Kautz: Superpixel Sampling Networks. IEEE ECCV, 2018.

Paper-3: T. Chen, S. Kornblith, M. Norouzi, G. Hinton: A Simple Framework for Contrastive Learning of Visual Representations.

Paper-3 :

2020.02.24: 柯芷莹 (Depthwise separable convolution)
2020.03.02: 孙孟柯 (Federated optimization)

Paper-1: H. B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. Arcas: Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS, 2017.

Paper-2: T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, V. Smith: Federated Optimization in Heterogeneous Networks. MLSys, 2020.

Paper-3: R. Shao, H. He, H. Liu, D. Liu: Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving. NeurIPS, 2019.

谢谢关注MIRACLE 奇迹研究组！欢迎垂询zhoushaohua[at]ict.ac.cn。

MIRACLE长期寻找：

合作伙伴（医院、医疗设备商、科研机构等）；
助理研究员、博士后和工程人员等；
博士硕士研究生、实习生（全职或远程）等。