162 citations · 182 across the 10 of their papers we have counts for
11 papers
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancer
Ruqayya Awan, Mohammed Nimir, Shan E Ahmed Raza +5
The accurate diagnosis and molecular profiling of colorectal cancers are critical for planning the best treatment options for patients. Microsatellite instability (MSI) or mismatch…
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…
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…
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…