no code implementations • 28 Feb 2022 • Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Fayyaz Minhas, David Snead, Nasir Rajpoot
In this paper we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumen and different tissue regions that leverages data from multiple independent data sources.
no code implementations • 24 Feb 2022 • Ruqayya Awan, Mohammed Nimir, Shan E Ahmed Raza, Mohsin Bilal, Johannes Lotz, David Snead, Andrew Robinson, Nasir Rajpoot
Unlike previous studies on MSI prediction involving training a CNN using coarse labels (MSI vs Microsatellite Stable (MSS)), we have utilised fine-grain MMR labels for training purposes.
no code implementations • 17 Dec 2021 • Amina Asif, Kashif Rajpoot, David Snead, Fayyaz Minhas, Nasir Rajpoot
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-resolution images of tissue slides.
no code implementations • 29 Nov 2021 • Simon Graham, Mostafa Jahanifar, Quoc Dang Vu, Giorgos Hadjigeorghiou, Thomas Leech, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir Rajpoot
The challenge encourages researchers to develop algorithms that perform segmentation, classification and counting of nuclei within the current largest known publicly available nuclei-level dataset in CPath, containing around half a million labelled nuclei.
no code implementations • 25 Aug 2021 • Simon Graham, Mostafa Jahanifar, Ayesha Azam, Mohammed Nimir, Yee-Wah Tsang, Katherine Dodd, Emily Hero, Harvir Sahota, Atisha Tank, Ksenija Benes, Noorul Wahab, Fayyaz Minhas, Shan E Ahmed Raza, Hesham El Daly, Kishore Gopalakrishnan, David Snead, Nasir Rajpoot
The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers.
no code implementations • 25 Jun 2021 • Noorul Wahab, Islam M Miligy, Katherine Dodd, Harvir Sahota, Michael Toss, Wenqi Lu, Mostafa Jahanifar, Mohsin Bilal, Simon Graham, Young Park, Giorgos Hadjigeorghiou, Abhir Bhalerao, Ayat Lashen, Asmaa Ibrahim, Ayaka Katayama, Henry O Ebili, Matthew Parkin, Tom Sorell, Shan E Ahmed Raza, Emily Hero, Hesham Eldaly, Yee Wah Tsang, Kishore Gopalakrishnan, David Snead, Emad Rakha, Nasir Rajpoot, Fayyaz Minhas
The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
no code implementations • 6 Mar 2020 • Jevgenij Gamper, Brandon Chan, Yee Wah Tsang, David Snead, Nasir Rajpoot
To train a robust deep learning model, one usually needs a balanced set of categories in the training data.
no code implementations • 22 Jul 2019 • Muhammad Shaban, Ruqayya Awan, Muhammad Moazam Fraz, Ayesha Azam, David Snead, Nasir M. Rajpoot
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.
no code implementations • 5 Jun 2018 • Simon Graham, Hao Chen, Jevgenij Gamper, Qi Dou, Pheng-Ann Heng, David Snead, Yee Wah Tsang, Nasir Rajpoot
However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures.
Ranked #3 on
Colorectal Gland Segmentation:
on CRAG
no code implementations • 23 Jan 2018 • Korsuk Sirinukunwattana, David Snead, David Epstein, Zia Aftab, Imaad Mujeeb, Yee Wah Tsang, Ian Cree, Nasir Rajpoot
Distant metastasis is the major cause of death in colorectal cancer (CRC).
no code implementations • 23 May 2017 • Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb, Sai Dileep Munugoti, Vamsi Tallam, Tomi Pitkäaho, Taina Lehtimäki, Thomas Naughton, Matt Berseth, Aníbal Pedraza, Ramakrishnan Mukundan, Matthew Smith, Abhir Bhalerao, Erik Rodner, Marcel Simon, Joachim Denzler, Chao-Hui Huang, Gloria Bueno, David Snead, Ian Ellis, Mohammad Ilyas, Nasir Rajpoot
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.