12 citations · 21 across the 6 of their papers we have counts for
7 papers
AI-assisted mitotic counting improves reproducibility and efficiency across multiple tumour types
Simon Graham, Mostafa Jahanifar, Quoc Dang Vu +15
Mitotic counting is an important component of tumour grading, diagnosis and prognostic assessment across several tumour types, but manual assessment is time-consuming and subject t…
Potential Role of Agentic Artificial Intelligence in Toxicologic Pathology
Nasir Rajpoot, Richard Haworth, Xavier Palazzi +14
As the volume and complexity of nonclinical toxicology studies continue to increase, toxicologic pathology reporting faces persistent challenges, including fragmented sources of da…
CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment
Esha Sadia Nasir, Behnaz Elhaminia, Mark Eastwood +9
Accurate and efficient registration of whole slide images (WSIs) is essential for high-resolution, nuclei-level analysis in multi-stained tissue slides. We propose a novel coarse-t…
The case for delegated AI autonomy for Human AI teaming in healthcare
Yan Jia, Harriet Evans, Zoe Porter +5
In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act…
CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns
Arwa Al-Rubaian, Gozde N. Gunesli, Wajd A. Althakfi +4
Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification of such patterns is crucial du…
Large Multimodal Model based Standardisation of Pathology Reports with Confidence and their Prognostic Significance
Ethar Alzaid, Gabriele Pergola, Harriet Evans +2
Pathology reports are rich in clinical and pathological details but are often presented in free-text format. The unstructured nature of these reports presents a significant challen…
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…