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20182023
most citedDeep Learning Models for Digital Pathology

10 citations · 11 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CV2023

Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images

Qiyuan Hu, Abbas A. Rizvi, Geoffery Schau +13

Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in pa…

cs.CV2023

Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas from Hematoxylin and Eosin Images

Kshitij Ingale, Sun Hae Hong, Josh S. K. Bell +8

MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC) and is the subject of active drug development. Challenges in identifying patients for these t…

cs.CV20221 cited

AI-augmented histopathologic review using image analysis to optimize DNA yield and tumor purity from FFPE slides

Bolesław L. Osinski, Aïcha BenTaieb, Irvin Ho +8

To achieve minimum DNA input and tumor purity requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Misestima…

cs.CV201910 cited

Deep Learning Models for Digital Pathology

Aïcha BenTaieb, Ghassan Hamarneh

Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic med…

cs.CV2018

Select, Attend, and Transfer: Light, Learnable Skip Connections

Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma +8

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risk…