70 citations · 232 across the 36 of their papers we have counts for
38 papers
SAIL: A Learnable Generalisation of Spatial Autocorrelation over Graph-Structured Data
Piotr Keller, Fayyaz Minhas
In vision and scientific domains involving spatial data, a key question is whether node feature values vary independently across space or form coherent spatial structures. Classica…
The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models
Dhyey Yajnik, Amina Asif, Fayyaz Minhas
How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation models (PFMs) and ResNet baseli…
DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning
George Wright, Slawomir Michniewski, Eleanor Jameson +1
Background: Phage therapy shows promise for treating antibiotic-resistant Klebsiella infections. Identifying phage depolymerases that target Klebsiella capsular polysaccharides is…
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling
Piotr Keller, Muhammad Dawood, Brinder Singh Chohan +1
Machine learning in computational pathology (CPath) often aggregates patch-level predictions from multi-gigapixel Whole Slide Images (WSIs) to generate WSI-level prediction scores…
TIAViz: A Browser-based Visualization Tool for Computational Pathology Models
Mark Eastwood, John Pocock, Mostafa Jahanifar +8
Digital pathology has gained significant traction in modern healthcare systems. This shift from optical microscopes to digital imagery brings with it the potential for improved dia…
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…
A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia
Adam J Shephard, Raja Muhammad Saad Bashir, Hanya Mahmood +11
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra- observer varia…
Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout
Srijay Deshpande, Fayyaz Minhas, Nasir Rajpoot
Generating realistic tissue images with annotations is a challenging task that is important in many computational histopathology applications. Synthetically generated images and an…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images
Mark Eastwood, Heba Sailem, Silviu Tudor +10
Malignant mesothelioma is classified into three histological subtypes, Epithelioid, Sarcomatoid, and Biphasic according to the relative proportions of epithelioid and sarcomatoid t…
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Images
Piotr Keller, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas
How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determina…
Nuclear Segmentation and Classification: On Color & Compression Generalization
Quoc Dang Vu, Robert Jewsbury, Simon Graham +5
Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize we…
SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas +1
Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespo…
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin +4
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, va…
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…
Insights into performance evaluation of com-pound-protein interaction prediction methods
Adiba Yaseen, Imran Amin, Naeem Akhter +2
Motivation: Machine learning based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing studies and can improve the efficiency…
REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology
Alex Foote, Amina Asif, Nasir Rajpoot +1
Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in ra…
Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits
Amina Asif, Kashif Rajpoot, David Snead +2
Computational Pathology (CPath) is an emerging field concerned with the study of tissue pathology via computational algorithms for the processing and analysis of digitized high-res…
CoNIC: Colon Nuclei Identification and Counting Challenge 2022
Simon Graham, Mostafa Jahanifar, Quoc Dang Vu +6
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be…
SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer
Wenqi Lu, Michael Toss, Emad Rakha +2
Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive factor which is overexpressed in 15-20% of breast cancer (BCa). The determination of its s…
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge
Mostafa Jahanifar, Adam Shephard, Neda Zamani Tajeddin +5
The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItos…
All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning
Muhammad Dawood, Kim Branson, Nasir M. Rajpoot +1
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption chara…
ALBRT: Cellular Composition Prediction in Routine Histology Images
Muhammad Dawood, Kim Branson, Nasir M. Rajpoot +1
Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosi…
L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
Fayyaz Minhas, Michael S. Toss, Noor ul Wahab +2
Can we predict if an early stage cancer patient is at high risk of developing distant metastasis and what clinicopathological factors are associated with such a risk? In this paper…