480 citations · 509 across the 14 of their papers we have counts for
16 papers
AI-assisted mitotic counting improves reproducibility and efficiency across multiple tumour types
Simon Graham, Mostafa Jahanifar, Quoc Dang Vu +15
Mitotic counting is an important component of tumour grading, diagnosis and prognostic assessment across several tumour types, but manual assessment is time-consuming and subject t…
MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis
Trinh T. L. Vuong, Simon Graham, Quoc Dang Vu +5
Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type and image acquisition. We present…
Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
Shubham Innani, Suhang You, Adam Shephard +13
Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and treatment prediction from gig…
Multimodal Oncology Agent for IDH1 Mutation Prediction in Low-Grade Glioma
Hafsa Akebli, Adam Shephard, Vincenzo Della Mea +1
Low-grade gliomas frequently present IDH1 mutations that define clinically distinct subgroups with specific prognostic and therapeutic implications. This work introduces a Multimod…
CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment
Esha Sadia Nasir, Behnaz Elhaminia, Mark Eastwood +9
Accurate and efficient registration of whole slide images (WSIs) is essential for high-resolution, nuclei-level analysis in multi-stained tissue slides. We propose a novel coarse-t…
A Novel Approach to Linking Histology Images with DNA Methylation
Manahil Raza, Muhammad Dawood, Talha Qaiser +1
DNA methylation is an epigenetic mechanism that regulates gene expression by adding methyl groups to DNA. Abnormal methylation patterns can disrupt gene expression and have been li…
From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review
Behnaz Elhaminia, Abdullah Alsalemi, Esha Nasir +6
Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information betwee…
CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns
Arwa Al-Rubaian, Gozde N. Gunesli, Wajd A. Althakfi +4
Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification of such patterns is crucial du…
Benchmarking Domain Generalization Algorithms in Computational Pathology
Neda Zamanitajeddin, Mostafa Jahanifar, Kesi Xu +2
Deep learning models have shown immense promise in computational pathology (CPath) tasks, but their performance often suffers when applied to unseen data due to domain shifts. Addr…
On generalisability of segment anything model for nuclear instance segmentation in histology images
Kesi Xu, Lea Goetz, Nasir Rajpoot
Pre-trained on a large and diverse dataset, the segment anything model (SAM) is the first promptable foundation model in computer vision aiming at object segmentation tasks. In thi…
An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer
Adam J Shephard, Mostafa Jahanifar, Ruoyu Wang +6
Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast c…
Transformer-based Model for Oral Epithelial Dysplasia Segmentation
Adam J Shephard, Hanya Mahmood, Shan E Ahmed Raza +12
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. OED grading is subject to large inter/intra-rater variability, re…
Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images
Arwa Al-Rubaian, Gozde N. Gunesli, Wajd A. Althakfi +3
Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns. The quantity of these patterns can be related to tumor beh…
Domain Generalization in Computational Pathology: Survey and Guidelines
Mostafa Jahanifar, Manahil Raza, Kesi Xu +8
Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis application…
One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +4
The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially lo…
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Mitko Veta, Paul J. van Diest, Stefan M. Willems +26
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be on…