15 citations · 33 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge
Adam Shephard, Mostafa Jahanifar, Ruoyu Wang +6
The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an e…
Cellular Segmentation and Composition in Routine Histology Images using Deep Learning
Muhammad Dawood, Raja Muhammad Saad Bashir, Srijay Deshpande +2
Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computatio…
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…
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…
A Generalized Meta-loss function for regression and classification using privileged information
Amina Asif, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas
Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informati…