70 citations · 232 across the 36 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…
Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations
Noorul Wahab, Islam M Miligy, Katherine Dodd +24
Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, an…
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology
Alex Foote, Amina Asif, Ayesha Azam +3
Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance. Typically, the generalization perform…
SAFRON: Stitching Across the Frontier for Generating Colorectal Cancer Histology Images
Srijay Deshpande, Fayyaz Minhas, Simon Graham +1
Synthetic images can be used for the development and evaluation of deep learning algorithms in the context of limited availability of data. In the field of computational pathology,…