12 citations · 45 across the 16 of their papers we have counts for
16 papers
Computationally Efficient Pathology Segmentation using Knowledge Distillation from Foundation Models
Jiaqi Lv, Yijie Zhu, Saki Okada +4
Automatic tissue segmentation is essential for large-scale analysis of histopathology whole-slide images (WSIs), but accurate pixel-level segmentation remains challenging. Pixel-le…
KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images
Jiaqi Lv, Esha Sadia Nasir, Kesi Xu +4
Accurate detection and classification of nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning arc…
Nuclei-Location Based Point Set Registration of Multi-Stained Whole Slide Images
Adith Jeyasangar, Abdullah Alsalemi, Shan E Ahmed Raza
Whole Slide Images (WSIs) provide exceptional detail for studying tissue architecture at the cell level. To study tumour microenvironment (TME) with the context of various protein…
An AI based Digital Score of Tumour-Immune Microenvironment Predicts Benefit to Maintenance Immunotherapy in Advanced Oesophagogastric Adenocarcinoma
Quoc Dang Vu, Caroline Fong, Anderley Gordon +7
Gastric and oesophageal (OG) cancers are the leading causes of cancer mortality worldwide. In OG cancers, recent studies have showed that PDL1 immune checkpoint inhibitors (ICI) in…
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…
Transformer-based Model for Oral Epithelial Dysplasia Segmentation
Adam J Shephard, Hanya Mahmood, Shan E Ahmed Raza +12
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. OED grading is subject to large inter/intra-rater variability, re…
Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images
Arwa Al-Rubaian, Gozde N. Gunesli, Wajd A. Althakfi +3
Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns. The quantity of these patterns can be related to tumor beh…
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…
A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia
Adam J Shephard, Raja Muhammad Saad Bashir, Hanya Mahmood +11
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra- observer varia…
Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images
Raja Muhammad Saad Bashir, Talha Qaiser, Shan E Ahmed Raza +1
Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years…
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…
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin +4
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, va…
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…
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…
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…