ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar


  1. T. Kyono, F. Gilbert, M. van der Schaar, "Improving Workflow Efficiency for Mammography Using Machine Learning," Journal of the American College of Radiology, 2019.
  2. B. Lim, M. van der Schaar, "Disease-Atlas: Navigating Disease Trajectories using Deep Learning," Machine Learning for Healthcare Conference (MLHC), 2018. [Link] [Presentation] - Best Paper Award in IJCAI-BOOM Workshop
  3. D. Jarrett, J. Yoon, and M. van der Schaar, "MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]
  4. T. Kyono, F. J. Gilbert, and M. van der Schaar, "MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis," Submitted, 2018. [Link]
  5. J. Yoon, W. R. Zame and M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks," IEEE Transactions on Biomedical Engineering, 2018. [Link]
  6. J. Yoon, W. R. Zame, M. van der Schaar, "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," ICLR, 2018. [Link]
  7. K. Ahuja, W. R. Zame, M. van der Schaar, "DPSCREEN: Dynamic Personalized Screening," NIPS, 2017. [Link][Poster]
  8. A. Alaa, K. H. Moon, W. Hsu and M. van der Schaar, "ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening," IEEE Transactions on Multimedia - Special Issue on Multimedia-based Healthcare, 2016. [Link]