illustration of DNA strand and abstract shapes on a computer screen on a green gradient background

Medical, health, and genomics

Digital pathology helps decode tumor microenvironments for precision immunotherapy. GigaPath is a novel vision transformer that can scale to gigapixel whole-slide images by adapting dilated attention for digital pathology. In joint work with Providence and UW, we’re sharing Prov-GigaPath, the first whole-slide pathology foundation model pretrained on large-scale real-world data, for advancing clinical research and discovery.

GigaPath: Whole-Slide Foundation Model for Digital Pathology

Research Forum Ep2 | Panel | Transforming the Natural Sciences with AI

Research Forum 2 | Panel Discussion: Transforming the Natural Sciences with AI

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Scaling early detection of esophageal cancer with AI

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Published Date

Publication

Deep Learning with Mixed Supervision for Brain Tumor Segmentation

Pawel Mlynarski, Herve' Delingette, Antonio Criminisi, Nicholas Ayache

December 2018

Project

Tool

Because Microsoft Genomics is on Azure, you have the performance and scalability of a world-class supercomputing center, on demand in the cloud. Take advantage of a backend network with MPI latency under three microseconds and…

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Publication

Extrinsic Noise Suppression in Micro RNA Mediated Incoherent Feedforward Loops

Sumit Mukherjee, Alberto Carignano, Abhyudai Singh, Georg Seelig

Conference on Decision and Control (CDC) | December 2018

Publication

Modeling influenza-like illnesses through composite compartmental models

Nir Levy, Michael Iv, Elad Yom-Tov

Physica A | December 2018, Vol 494: pp. 288-293

Project

Publication

PKNOX2 expression and regulation in the bone marrow mesenchymal stem cells of Fanconi anemia patients and healthy donors

Ilgin Cagnan, Erdal Cosgun, Ozlen Konu, Duygu Uckan, Aysen Gunel Ozcan

Vol.:(0123456789) 1 3 Molecular Biology Reports | December 2018

Tool

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

Implementation of TACL 2017 paper: “Cross-Sentence N-ary Relation Extraction with Graph LSTMs.” Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova and Wen-tau Yih.

GitHub Publication

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