Other

Amazon Technologies, Inc.

Hamed Pirsiavash

Vineet Sachdev

Ravindra Kompella

Sheng Chen

Todd Hester

Hitesh Arora

Michael Lawrence LeKander

Ayan Tuhinendu Sinha

Mark Buckler

Techniques are generally described for fusing sensor data of different modalities using a transformer. In various examples, first sensor data may be received from a first sensor and second sensor data may be received from a second sensor. A first feature representation of the first sensor data may be generated using a first machine learning model and a second feature representation of the second sensor data may be generated using a second machine learning model. In some examples, a modified first feature representation of the first sensor data may be generated based at least in part on a self-attention mechanism of a transformer encoder. The modified first feature representation may be generated based at least in part on the first feature representation and the second feature representation. A computer vision task may be performed using the modified first feature representation.

Sensor data fusion using cross-modal transformer

A set of enhancements to further improve the performance of deep learning artificial intelligence algorithms trained to detect and localize colon polyps. The enhancements spanning training data mining efficiencies and automation, training data augmentation, early detection of polyps enable a more performant colon polyp detection solution for use on colonoscopy procedure recordings or live procedures in endoscopy centers.

Deep learning for real-time colon polyp detection

Growing community of inventors

Ellicott City, MD, United States of America

Hamed Pirsiavash