5 papers
Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Ole-Johan Skrede, Manohar Pradhan, Maria Xepapadakis Isaksen +20
Deep learning is expected to aid pathologists by automating tasks such as tumour segmentation. We aimed to develop one universal tumour segmentation model for histopathological ima…
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…
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…
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…
Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images
Masoud Tafavvoghi, Anders Sildnes, Mehrdad Rakaee +4
Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for…