AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases

1 code implementation • 12 Dec 2020 • Jianan Chen, Helen M. C. Cheung, Laurent Milot, Anne L. Martel

Experimental results empirically validated our hypothesis that incorporating imaging features of all lesions improves outcome prediction for multifocal cancer.

Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model

no code implementations • 6 Sep 2019 • Jianan Chen, Laurent Milot, Helen M. C. Cheung, Anne L. Martel

The performance of the proposed pipeline was evaluated on an institutional MRI cohort of 108 patients with colorectal cancer liver metastases.

Clustering Representation Learning