387 citations · 2k across the 49 of their papers we have counts for
52 papers
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…
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…
FedDropoutAvg: Generalizable federated learning for histopathology image classification
Gozde N. Gunesli, Mohsin Bilal, Shan E Ahmed Raza +1
Federated learning (FL) enables collaborative learning of a deep learning model without sharing the data of participating sites. FL in medical image analysis tasks is relatively ne…
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…
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…
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 QuadTree Image Representation for Computational Pathology
Rob Jewsbury, Abhir Bhalerao, Nasir Rajpoot
The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images. Histopathology images are large and need to…
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…
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
Simon Graham, Mostafa Jahanifar, Ayesha Azam +14
The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers. Yet, there is a major b…
Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification
Neda Zamanitajeddin, Mostafa Jahanifar, Nasir Rajpoot
Digitization of histology images and the advent of new computational methods, like deep learning, have helped the automatic grading of colorectal adenocarcinoma cancer (CRA). Prese…
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…
Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles
Jevgenij Gamper, Nasir Rajpoot
We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH o…
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…
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram +2
While high-resolution pathology images lend themselves well to `data hungry' deep learning algorithms, obtaining exhaustive annotations on these images is a major challenge. In thi…