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Archives: Resources post

Description.

Deep Learning-Based Evaluation of PD-L1 Immunohistochemistry-Stained Slides for Robust Tumor Proportion Scoring Enables Better Categorization of Non-Small-Cell Lung Cancer Cases

USCAP 2023

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

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