387 citations · 2k across the 49 of their papers we have counts for
19 papers · 1 filter
On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks
Komal Mariam, Osama Mohammed Afzal, Wajahat Hussain +5
Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide di…
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…
Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images
Quoc Dang Vu, Kashif Rajpoot, Shan E Ahmed Raza +1
Diagnostic, prognostic and therapeutic decision-making of cancer in pathology clinics can now be carried out based on analysis of multi-gigapixel tissue images, also known as whole…
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…
Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia
Adam J. Shephard, Simon Graham, R. M. Saad Bashir +4
Oral epithelial dysplasia (OED) is a pre-malignant histopathological diagnosis given to lesions of the oral cavity. Predicting OED grade or whether a case will transition to malign…
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
Mostafa Jahanifar, Neda Zamani Tajeddin, Navid Alemi Koohbanani +1
From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue…
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…
A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma
Muhammad Shaban, Shan E Ahmed Raza, Mariam Hassan +11
The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim…
Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis
Nima Hatami, Mohsin Bilal, Nasir Rajpoot
The problem of recognizing various types of tissues present in multi-gigapixel histology images is an important fundamental pre-requisite for downstream analysis of the tumor micro…
Classification of COVID-19 via Homology of CT-SCAN
Sohail Iqbal, H. Fareed Ahmed, Talha Qaiser +2
In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic. There…
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,…
PanNuke Dataset Extension, Insights and Baselines
Jevgenij Gamper, Navid Alemi Koohbanani, Ksenija Benes +6
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in who…
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Simon Graham, David Epstein, Nasir Rajpoot
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior k…
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
Jevgenij Gamper, Brandon Chan, Yee Wah Tsang +2
To train a robust deep learning model, one usually needs a balanced set of categories in the training data. The data acquired in a medical domain, however, frequently contains an a…
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Yanning Zhou, Simon Graham, Navid Alemi Koohbanani +3
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tis…
Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework
Navid Alemi Koohbanani, Mostafa Jahanifar, Ali Gooya +1
Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segm…
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…