A novel deep learning pipeline for cell typing and phenotypic marker quantification in multiplex imaging
BioRxiv
Predicting response to immune checkpoint inhibitors (ICI) in non-small-cell lung cancer (NSCLC) by combining spatial analysis of cells and RNA sequencing data from biopsies using deep learning (DL)
SITC 2022
Predicting CD8+ cell density and tumor-immune phenotypes in non-small-cell lung cancer (NSCLC) from standard H&E slides using deep learning (DL)
SITC 2022
A deep learning analysis pipeline for multiplex imaging identifies spatial features associated with clinical outcome in colorectal cancer
SITC 2022
Predicting response to naratuximab emtansine, an anti-CD37 antibody-drug conjugate (ADC), in combination with rituximab in Diffuse Large B Cell Lymphoma (DLBCL), by analyzing the spatial arrangement of CD37 and CD20 positive cells using deep learning
ENA 2022
Generating Virtual Multiplex Images from Sequential Immunohistochemistry (IHC) Slides using Deep Learning
Pathology Visions 2022
Predicting response to pembrolizumab in non-small cell lung cancer using spatial analysis of biopsy images by deep learning
ESMO 2022
Predicting response to naratuximab emtansine & rituximab in DLBCL / ASCO 2022
ASCO 2022
Predicting Response to Pembrolizumab in NSCLC by Analyzing the Spatial Arrangement of TILs Using Deep Learning / ASCO 2021
ASCO 2021
The Predictive Potential of the Spatial Signature of Lymphocytes In Breast Cancer Patients
ASCO 2020
