Arizona State University

Entit Software LLC

Banner Health

Fei Gao

Teresa Wu

Anurag Singla

Zhipeng Zhao

Yanzhe Xu

Yi Su

Jay Shah

A system for blob detection using deep learning is disclosed. The system may include a non-transitory computer-readable storage medium configured to store a plurality of instructions thereon which, when executed by a processor, cause the system to train a U-Net and generate a probability map including a plurality of centroids of a plurality of corresponding blobs, derive two distance maps with bounded probabilities, apply Difference of Gaussian (DoG) with an adaptive scale constrained by the two distance maps with the bounded probabilities, and apply Hessian analysis and perform a blob segmentation.

Deep learning based blob detection systems and methods

Using deep learning, imaging harmonization among images produced with differing PET tracers is achieved. A method may include providing an original PET image of the brain using an original PET tracer, providing a target PET tracer, and converting, using a deep learning neural network, the original PET image into a target PET image simulating the image that would be obtained had the target PET tracer been used.

Deep residual inception encoder-decoder network for amyloid PET harmonization

Example embodiments disclosed herein relate to distributed pattern discovery. A local frequent pattern tree or local frequent pattern trees can be merged. The merging can be based on activities or transactions associated with the local frequent pattern tree or trees.

Distributed pattern discovery

Growing community of inventors

Sunnyvale, CA, United States of America

Fei Gao