Search Results for author: Nasir Rajpoot

Found 49 papers, 11 papers with code

TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge

1 code implementation23 Jun 2022 Adam Shephard, Mostafa Jahanifar, Ruoyu Wang, Muhammad Dawood, Simon Graham, Kastytis Sidlauskas, Syed Ali Khurram, Nasir Rajpoot, Shan E Ahmed Raza

The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model.

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.

Deep Feature based Cross-slide Registration

no code implementations21 Feb 2022 Ruqayya Awan, Shan E Ahmed Raza, Johannes Lotz, Nick Weiss, Nasir Rajpoot

During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block.

Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images

no code implementations14 Feb 2022 Quoc Dang Vu, Kashif Rajpoot, Shan E Ahmed Raza, Nasir Rajpoot

Based on our experiments involving various datasets consisting of a total of 5, 306 WSIs, the results demonstrate that H2T based holistic WSI-level representations offer competitive performance compared to recent state-of-the-art methods and can be readily utilized for various downstream analysis tasks.

Decision Making Natural Language Processing +1

REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology

1 code implementation28 Jan 2022 Alex Foote, Amina Asif, Nasir Rajpoot, Fayyaz Minhas

Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows.

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

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer

1 code implementation12 Oct 2021 Wenqi Lu, Michael Toss, Emad Rakha, Nasir Rajpoot, Fayyaz Minhas

The network was trained and tested on slides from The Cancer Genome Atlas (TCGA) in addition to two independent test datasets.

Decision Making whole slide images

Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge

no code implementations2 Sep 2021 Mostafa Jahanifar, Adam Shephard, Neda Zamani Tajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz Minhas, Nasir Rajpoot

The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading.

Domain Generalization

Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input

no code implementations30 Aug 2021 Mostafa Jahanifar, Neda Zamani Tajeddin, Navid Alemi Koohbanani, Nasir Rajpoot

From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite.

Interactive Segmentation Semantic Segmentation

A QuadTree Image Representation for Computational Pathology

no code implementations24 Aug 2021 Rob Jewsbury, Abhir Bhalerao, Nasir Rajpoot

The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images.

Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification

1 code implementation29 Jun 2021 Neda Zamanitajeddin, Mostafa Jahanifar, Nasir Rajpoot

To tackle these challenges, we propose to use a statistical network analysis method to describe the complex structure of the tissue micro-environment by modelling nuclei and their connections as a network.

Image Classification

Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology

no code implementations14 Jun 2021 Alex Foote, Amina Asif, Ayesha Azam, Tim Marshall-Cox, Nasir Rajpoot, Fayyaz Minhas

Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance.

Adversarial Attack

A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

no code implementations16 Apr 2021 Muhammad Shaban, Shan E Ahmed Raza, Mariam Hassan, Arif Jamshed, Sajid Mushtaq, Asif Loya, Nikolaos Batis, Jill Brooks, Paul Nankivell, Neil Sharma, Max Robinson, Hisham Mehanna, Syed Ali Khurram, Nasir Rajpoot

In this study, our aim is to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI based automated method.

Clinical Knowledge whole slide images

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Ben Glocker, Patrick Godau, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Bernhard Kainz, Emre Kavur, Hannes Kenngott, Jens Kleesiek, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clarisa Sanchez Gutierrez, Julien Schroeter, Anindo Saha, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation object-detection +2

Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis

no code implementations1 Apr 2021 Nima Hatami, Mohsin Bilal, Nasir Rajpoot

In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images.

Dictionary Learning

Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles

1 code implementation CVPR 2021 Jevgenij Gamper, Nasir Rajpoot

We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks.

Image Captioning Multi-Task Learning

Classification of COVID-19 via Homology of CT-SCAN

no code implementations21 Feb 2021 Sohail Iqbal, H. Fareed Ahmed, Talha Qaiser, Muhammad Imran Qureshi, Nasir Rajpoot

In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic.

Classification Computed Tomography (CT) +2

Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations

no code implementations12 Aug 2020 Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram, Pavitra Krishnaswamy, Nasir Rajpoot

In this paper, we propose a self-supervised CNN approach to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images.

Domain Adaptation General Classification +1

SAFRON: Stitching Across the Frontier for Generating Colorectal Cancer Histology Images

no code implementations11 Aug 2020 Srijay Deshpande, Fayyaz Minhas, Simon Graham, Nasir Rajpoot

Compared to other existing approaches, our framework is efficient in terms of the memory requirements for training and also in terms of the number of computations to construct a large high-resolution image.

NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images

2 code implementations29 May 2020 Navid Alemi Koohbanani, Mostafa Jahanifar, Neda Zamani Tajadin, Nasir Rajpoot

As nuclei, cells and glands are fundamental objects for downstream analysis in computational pathology/cytology, in this paper we propose a simple CNN-based approach to speed up collecting annotations for these objects which requires minimum interaction from the annotator.

Instance Segmentation Interactive Segmentation +1

PanNuke Dataset Extension, Insights and Baselines

5 code implementations24 Mar 2020 Jevgenij Gamper, Navid Alemi Koohbanani, Ksenija Benes, Simon Graham, Mostafa Jahanifar, Syed Ali Khurram, Ayesha Azam, Katherine Hewitt, Nasir Rajpoot

The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides.

Selection bias whole slide images

NuClick: From Clicks in the Nuclei to Nuclear Boundaries

no code implementations7 Sep 2019 Mostafa Jahanifar, Navid Alemi Koohbanani, Nasir Rajpoot

Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data.

Instance Segmentation Nuclear Segmentation +1

CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

no code implementations3 Sep 2019 Yanning Zhou, Simon Graham, Navid Alemi Koohbanani, Muhammad Shaban, Pheng-Ann Heng, Nasir Rajpoot

Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity.

Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework

no code implementations27 Aug 2019 Navid Alemi Koohbanani, Mostafa Jahanifar, Ali Gooya, Nasir Rajpoot

Spectral clustering method is applied on the output of the last SpaNet, which utilizes the nuclear mask and the Gaussian-like detection map for determining the connected components and associated cluster identifiers, respectively.

Instance Segmentation Nuclear Segmentation +1

Nuclei Detection Using Mixture Density Networks

no code implementations22 Aug 2018 Navid Alemi Koohababni, Mostafa Jahanifar, Ali Gooya, Nasir Rajpoot

Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc.

Cell Segmentation

Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

no code implementations ECCV 2018 Haroon Idrees, Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Al-Maadeed, Nasir Rajpoot, Mubarak Shah

With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision.

Crowd Counting Management +1

Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

no code implementations31 Jul 2018 Saad Ullah Akram, Talha Qaiser, Simon Graham, Juho Kannala, Janne Heikkilä, Nasir Rajpoot

In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs.

Mitosis Detection whole slide images

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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