6 papers
CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H&E Whole-Slide Images
Falah Jabar, Pasquale Lombardi, Aria Torkpour +10
Accurate nuclei detection and classification in hematoxylin and eosin (H and E) whole-slide images (WSIs) is a key task in computational pathology, particularly for quantitative an…
Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline
Masoud Tafavvoghi, Lars Ailo Bongo, André Berli Delgado +5
In this study, we built an end-to-end tumor-infiltrating lymphocytes (TILs) assessment pipeline within QuPath, demonstrating the potential of easily accessible tools to perform com…
A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images
Nikita Shvetsov, Thomas K. Kilvaer, Masoud Tafavvoghi +4
Developing clinically useful cell-level analysis tools in digital pathology remains challenging due to limitations in dataset granularity, inconsistent annotations, high computatio…
Open-source framework for detecting bias and overfitting for large pathology images
Anders Sildnes, Nikita Shvetsov, Masoud Tafavvoghi +5
Even foundational models that are trained on datasets with billions of data samples may develop shortcuts that lead to overfitting and bias. Shortcuts are non-relevant patterns in…
Fully Automatic Content-Aware Tiling Pipeline for Pathology Whole Slide Images
Falah Jabar, Lill-Tove Rasmussen Busund, Biagio Ricciuti +6
In recent years, the use of deep learning (DL) methods, including convolutional neural networks (CNNs) and vision transformers (ViTs), has significantly advanced computational path…
Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer
Nikita Shvetsov, Anders Sildnes, Masoud Tafavvoghi +5
Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) present…