Sony Group Corporation

Sony Corporation of America

Su-Hui Chiang

Ming-Chang Liu

Ko-Kai Albert Huang

Michael Zordan

Liyu Gong

A framework that includes one or more feature extractors (models) and a cluster component for clustering is described herein. The framework supports (1) offline image-based unsupervised clustering that replaces time-consuming manual gating; (2) online image-based single cell sorting. During training, one or multiple cell image datasets with or without ground truth are used to train one or more feature extractors, which are based on a neural network including several convolutional layers. Once trained, the feature extractors are used to extract features of cell images for unsupervised cell clustering and sorting. In addition, additional datasets may be used to further refine the feature extractors after they have been trained.

Image-based unsupervised multi-model cell clustering

A framework that includes a feature extractor and a cluster component for clustering is described herein. The framework supports (1) offline image-based unsupervised clustering that replaces time-consuming manual gating; (2) online image-based single cell sorting. During training, one or multiple cell image datasets with or without ground truth are used to train feature extractor, which is based on a neural network including several convolutional layers. Once trained, the feature extractor is used to extract features of cell images for unsupervised cell clustering and sorting. In addition, additional datasets may be used to further refine the feature extractor after it has been trained.

Framework for image based unsupervised cell clustering and sorting

An image-based classification workflow uses unsupervised clustering to help a user identify subpopulations of interest for sorting. Labeled cell images are used to fine-tune the supervised classification network for a specific experiment. The workflow allows the user to select the populations to sort in the same manner for a variety of applications. The supervised classification network is very fast, allowing it to make real-time sort decisions as the cell travels through a device. The workflow is more automated and has fewer user steps, which improves the ease of use. The workflow uses machine learning to avoid human error and bias from manual gating. The workflow does not require the user to be an expert in image processing, thus increasing the ease of use.

Classification workflow for flexible image based particle sorting

A framework combines vision and sample-efficient reinforcement-learning based on guided policy search for autonomous driving. A controller extracts environmental information from vision and is trained to drive using reinforcement learning.

Growing community of inventors

San Jose, CA, United States of America

Su-Hui Chiang