MICCAI 2021 Daily – Wednesday

An autoencoder-based multiple instance neural network sounds complicated, but it is actually fairly simple. Radiomic features are extracted from MRI scans, an autoencoder selects the features, and then a multiple instance neural network makes predictions based on those selected features. It is all connected and trained end to end. To the best of Jianan’s knowledge, this is the first work that has been designed specifically for predicting the outcome of cancer patients with multiple tumors . He spotted a gap because a large proportion of patients have multiple tumors, but existing techniques only look at the largest one or two lesions. Exploring all the tumors can lead to better treatment and improvements in prognosis. 8 DAILY MICCAI Wednesday Oral Presentation AMINN: Autoencoder-basedMultiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases Jianan Chen is a fourth-year PhD student at the University of Toronto under the supervision of Dr Anne Martel. He has developed an autoencoder- based multiple instance neural network for the prediction of survival rates of multifocal cancer patients. He speaks to us ahead of his oral presentation and poster session today. An overview of the proposed autoencoder-based multiple instance learning network. The network structure between the curly brackets is shared for each tumor of the same patient.