Merck Sharp + Dohme Corp.

Ronghua Chen

Robert Davidson

Albert Joseph Swiston

Antong Chen

Bo Jiang

Jun Zhuang

Rebecca D Argyros

Stephanie Nelson

Dani Kiyasseh

Robert C Davidson

Methods and systems are described for image segmentation. A machine learning model is applied to a set of images to generate results. The results may be obtained as a probability map for each image in the set of images. The model may be trained by accessing a set of labeled images, each image associated with a label indicating a location of a feature within a respective image. An initial set of parameters is accessed. An encoder is initialized with the initial set of parameters. The encoder is applied to the set of labeled images to generate a prediction of a feature location within each image. The initial set of parameters are updated based on the predictions and the label associated with the labeled images. The updated set of parameters and an additional set of parameters generated using a set of unlabeled images are aggregated.

Meta-learning for cardiac MRI segmentation

The present invention relates to novel engineered lower eukaryotic host cells for expressing heterologous proteins and to methods of generating such strains.

Engineered lower eukaryotic host strains for recombinant protein expression

Growing community of inventors

Needham Heights, MA, United States of America

Ronghua Chen