3 citations · 6 across the 7 of their papers we have counts for
7 papers
INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer
Piotr Keller, Mark Eastwood, Zedong Hu +15
Routine histology contains rich prognostic information in stage II/III colorectal cancer, much of which is embedded in complex spatial tissue organisation. We present INSIGHT, a gr…
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…
Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images
Manahil Raza, Ruqayya Awan, Raja Muhammad Saad Bashir +2
Digital whole slide images (WSIs) are generally captured at microscopic resolution and encompass extensive spatial data. Directly feeding these images to deep learning models is co…
Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancer
Ruqayya Awan, Mohammed Nimir, Shan E Ahmed Raza +5
The accurate diagnosis and molecular profiling of colorectal cancers are critical for planning the best treatment options for patients. Microsatellite instability (MSI) or mismatch…
Deep Feature based Cross-slide Registration
Ruqayya Awan, Shan E Ahmed Raza, Johannes Lotz +2
Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. These biomarker stained slide…
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Muhammad Shaban, Ruqayya Awan, Muhammad Moazam Fraz +3
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally…
Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Ruqayya Awan, Navid Alemi Koohbanani, Muhammad Shaban +2
Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis. However, availability of a large dataset is a major prerequisite for trainin…