![]() |
![]() |
University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Medical Image Analysis with Data-efficient Learning
Medical Image Analysis with Data-efficient LearningAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Hong Duong. Medical imaging is a critical step in modern healthcare procedures. Accurate interpretation of medical images, e.g., CT and MRI , plays an essential role in computer-aided diagnosis, assessment, and therapy. While deep neural networks have made great progress in various medical image analysis applications, the success is largely attributed to the massive datasets with abundant annotations. However, collecting and labeling such large-scaled dataset is prohibitively expensive and time-consuming. In this talk, I will present our recent works on building data-efficient learning systems for medical image analysis, such as semi-supervised learning, multi-modality learning, and multi-site learning. The up-to-date progress and promising future directions will also be discussed. This talk is part of the Data Science and Computational Statistics Seminar series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsSERENE Group Seminar Series Cold atoms Artificial Intelligence and Natural Computation seminarsOther talksProvably Convergent Plug-and-Play Quasi-Newton Methods for Imaging Inverse Problems [Friday seminar]: Irradiated brown dwarfs in the desert The percolating cluster is invisible to image recognition with deep learning Many-body localization from Hilbert- and real-space points of view Topological magnons and quantum magnetism Signatures of structural criticality and universality in the cellular anatomy of the brain |