Our goal is to advance pathology practice and discovery through development and validation of machine learning methods and software.
Dr. Cooper will speak at the 7th annual Digital Pathology & AI Congress hosted on May 26-27. The virtual meeting will feature presentations from academia and industry discussing the application of machine learning to primary diagnosis and clinical research and strategies needed for clinical deployment.
Dr. Cooper will speak at the PathLAKE 2022 Masterclass on May 18th. The Masterclass was created to provide pathologists and pathology trainees with an introduction to computational pathology.
The PharML workshop will be hosted this September at the ECML PKDD virtual meeting. This workshop brings together researchers from academia and industry to discuss the application of machine learning to healthcare and pharmaceutical data.
Mohamed Amgad received best poster award at the 2021 Association for Pathology Informatics Summit for his abstract "MuTILs: a multiresolution approach for computational TILs assessment using clinical guidelines".
Our tools and methods have enabled international crowds to generate over 200,000 annotations of breast cancer histology.
We are developing algorithms for accurate computational assessment of tumor-infiltrating lymphocytes in breast cancer.
We are developing an atlas of the rapid growth and senescence of the placenta - the black box in pregnancy.
We are developing a suite of tools to aid hematopathologists in diagnosis and prognostication for hematologic and lymphoid malignancies.
This project is creating new methods and tools to enable clinical translation of MR spectroscopy for radiation treatment planning.
This project is building next-generation survival analysis models using techniques like neural and Bayesian networks.
The Digital Slide Archive is an open-source platform for centralized management, sharing, annotating, and analyzing whole-slide image datasets.
HistomicsTK is an open-source library that provides developers with building-blocks for pathology image analysis.
HistomicsML is exploring unsupervised learning methods to build interactive tools for exploring datasets and for building machine-learning models.
Lee Cooper, PhD
Jeffrey Goldstein, MD/PhD
Mohamed Tageldin, MD
Pooya Mobadersany, PhD
Safoora Yousefi, PhD
Saumya Gurbani, PhD
Sanghoon Lee, PhD
Yue Hou, PhD
Jianpeng Xu, PhD
Matt Reyna, PhD
We are located on the 11th floor of the Arthur Rubloff Building on the Lake Michigan coast in downtown Chicago's Streeterville neighborhood. We are 5 blocks from the Chicago red line station and 2 blocks from the Intercampus Shuttle Ward Building stop.