Deep Learning for Biomedical Imaging
Biomedical imaging, such as cellular imaging, tissue imaging, medical imaging and organism imaging, is a gold mine for artificial intelligence and computer vision. Deep learning methods, including convolution neural networks, generative adversarial networks, autoencoders, have shown early success in segmentation, classification, regression applications, as well as potential in tasks such as registration, in silico labelling and gaining biological insights. This roundtable discussion aims to engage the audience to share opinions about the current status and future directions of deep learning for biomedical imaging.
*Which types of biomedical imaging data are available and relevant
*What are the current approaches and successes
*What are the future challenges and opportunities
Xian Zhang is leading a data science group in Novartis. Working with diverse biomedical imaging and sequencing data types, he and team focus on deep learning research and application in various segments of early drug discovery. Xian obtained his PhD from the University of Rochester and completed his postdoc in German Cancer Research Center.