Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

no code implementations • 12 Jan 2024 • Lingchao Mao, Hairong Wang, Leland S. Hu, Nhan L Tran, Peter D Canoll, Kristin R Swanson, Jing Li

Cancer remains one of the most challenging diseases to treat in the medical field.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

no code implementations • 30 Dec 2023 • Lujia Wang, Hairong Wang, Fulvio D'Angelo, Lee Curtin, Christopher P. Sereduk, Gustavo De Leon, Kyle W. Singleton, Javier Urcuyo, Andrea Hawkins-Daarud, Pamela R. Jackson, Chandan Krishna, Richard S. Zimmerman, Devi P. Patra, Bernard R. Bendok, Kris A. Smith, Peter Nakaji, Kliment Donev, Leslie C. Baxter, Maciej M. Mrugała, Michele Ceccarelli, Antonio Iavarone, Kristin R. Swanson, Nhan L. Tran, Leland S. Hu, Jing Li

The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images.

Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet

no code implementations • 4 Jun 2020 • Sara Ranjbar, Kyle W. Singleton, Lee Curtin, Cassandra R. Rickertsen, Lisa E. Paulson, Leland S. Hu, J. Ross Mitchell, Kristin R. Swanson

In this work we propose a deep learning approach for skull striping common MRI sequences in oncology such as T1-weighted with gadolinium contrast (T1Gd) and T2-weighted fluid attenuated inversion recovery (FLAIR) in patients with brain tumors.

Skull Stripping Specificity +1