Pathai, Inc.

Posh Development LLC

Kenneth Knute Leidal

Ilan N Wapinski

Oscar M Carrasco-zevallos

Jason Ku Wang

Maryam Pouryahya

Aditya Khosla

Andrew H Beck

Hunter L Elliott

Benjamin P Glass

Harsha Vardhan Pokkalla

Quang Huy Le

Amaro Taylor-weiner

Charles Biddle-Snead

Katy Wack

Murray Resnick

Aryan Pedawi

Amaro N Taylor-Weiner

Dinkar Juyal

Michael C Montalto

Zahil Shanis

In some aspects, the described systems and methods provide for a method for training a deep learning model to assess liver pathology, including accessing annotated liver pathology images associated with a group of patients in one or more randomized controlled clinical trials of nonalcoholic steatohepatitis therapy, each of the annotated liver pathology images including at least one annotation describing one or more tissue characteristic categories for a portion of the image, and training the deep learning model based on the annotated liver pathology images to predict the tissue characteristic categories, selected from a group comprising steatosis, lobular inflammation, hepatocyte ballooning, and fibrosis stage.

Systems and methods for assessing liver pathology

Techniques for classifying biomedical image data using a graph neural network are disclosed. In one particular embodiment, the techniques may be realized as a method for classifying biomedical image data comprising generating an annotated representation of biomedical image data; identifying a plurality of pixel clusters based on the biomedical image data; constructing a graph based on the plurality of pixel clusters; determining at least one biomedical feature for at least one node of the graph based on the annotated representation of the biomedical image data; and processing a graph neural network to classify the biomedical image data based on the at least one biomedical feature.

Systems and methods for classifying biomedical image data using a graph neural network

Described herein are techniques for suspending execution of a process, including through serializing execution state of the process. Through serializing the execution state, the execution state can be converted to a byte string for output. In some embodiments, an executing process that has been suspended may be resumed, through deserializing the execution state. For example, a byte string that is a serialized execution state of a suspended process may be deserialized to generate one or more data objects for the execution state of the suspended process, and a process may be configured with the data objects resulting from the deserializing. By configuring the process with the data objects resulting from the deserializing, the process may take on the execution state of the suspended executing process and resume execution from the point of suspension. The process that may be suspended may be an instance of a request/response application.

Serializing and/or deserializing programs with serializable state

Growing community of inventors

Cambridge, MA, United States of America

Kenneth Knute Leidal