Search Results for author: David Snead

Found 11 papers, 0 papers with code

One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification

no code implementations28 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.

Explainable Models Multi-Task Learning +2

Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancer

no code implementations24 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.

Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits

no code implementations17 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.

CoNIC: Colon Nuclei Identification and Counting Challenge 2022

no code implementations29 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.

Explainable Models Nuclear Segmentation

Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images

no code implementations22 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.

Representation Learning

MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images

no code implementations5 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.

Colorectal Gland Segmentation: Decision Making +3

Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues

no code implementations23 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.

whole slide images

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