@article{1439987,
      recid = {1439987},
      author = {Lian, Chunfeng, and Cao, Xiaohuan, and Rekik, Islem, and  Xu, Xuanang, and Yan, Pingkun,},
      title = {Machine learning in medical imaging : 12th International  Workshop, MLMI 2021 : held in conjunction with MICCAI 2021,  Strasbourg, France, September 27, 2021 : proceedings /.  MLMI (Workshop)},
      pages = {1 online resource :},
      note = {International conference proceedings.},
      abstract = {This book constitutes the proceedings of the 12th  International Workshop on Machine Learning in Medical  Imaging, MLMI 2021, held in conjunction with MICCAI 2021,  in Strasbourg, France, in September 2021.* The 71 papers  presented in this volume were carefully reviewed and  selected from 92 submissions. They focus on major trends  and challenges in the above-mentioned area, aiming to  identify new-cutting-edge techniques and their uses in  medical imaging. Topics dealt with are: deep learning,  generative adversarial learning, ensemble learning, sparse  learning, multi-task learning, multi-view learning,  manifold learning, and reinforcement learning, with their  applications to medical image analysis, computer-aided  detection and diagnosis, multi-modality fusion, image  reconstruction, image retrieval, cellular image analysis,  molecular imaging, digital pathology, etc. *The workshop  was held virtually.},
      url = {http://library.usi.edu/record/1439987},
      doi = {https://doi.org/10.1007/978-3-030-87589-3},
}