Search Results for author: Mostafa Jahanifar

Found 28 papers, 10 papers with code

TIAViz: A Browser-based Visualization Tool for Computational Pathology Models

1 code implementation15 Feb 2024 Mark Eastwood, John Pocock, Mostafa Jahanifar, Adam Shephard, Skiros Habib, Ethar Alzaid, Abdullah Alsalemi, Jan Lukas Robertus, Nasir Rajpoot, Shan Raza, Fayyaz Minhas

Throughout the development of a machine learning (ML) model in digital pathology, it is crucial to have flexible, openly available tools to visualize models, from their outputs and predictions to the underlying annotations and images used to train or test a model.

whole slide images

An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer

1 code implementation10 Nov 2023 Adam J Shephard, Mostafa Jahanifar, Ruoyu Wang, Muhammad Dawood, Simon Graham, Kastytis Sidlauskas, Syed Ali Khurram, Nasir M Rajpoot, Shan E Ahmed Raza

Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer.

whole slide images

Domain Generalization in Computational Pathology: Survey and Guidelines

no code implementations30 Oct 2023 Mostafa Jahanifar, Manahil Raza, Kesi Xu, Trinh Vuong, Rob Jewsbury, Adam Shephard, Neda Zamanitajeddin, Jin Tae Kwak, Shan E Ahmed Raza, Fayyaz Minhas, Nasir Rajpoot

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications.

Benchmarking Domain Generalization

A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia

no code implementations6 Jul 2023 Adam J Shephard, Raja Muhammad Saad Bashir, Hanya Mahmood, Mostafa Jahanifar, Fayyaz Minhas, Shan E Ahmed Raza, Kris D McCombe, Stephanie G Craig, Jacqueline James, Jill Brooks, Paul Nankivell, Hisham Mehanna, Syed Ali Khurram, Nasir M Rajpoot

To address this, we developed a novel artificial intelligence algorithm that can assign an Oral Malignant Transformation (OMT) risk score, based on histological patterns in the in Haematoxylin and Eosin stained whole slide images, to quantify the risk of OED progression.

whole slide images

Why is the winner the best?

no code implementations CVPR 2023 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.

Benchmarking Multi-Task Learning

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

1 code implementation11 Mar 2023 Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.

Nuclear Segmentation Segmentation +2

Nuclear Segmentation and Classification: On Color & Compression Generalization

no code implementations9 Jan 2023 Quoc Dang Vu, Robert Jewsbury, Simon Graham, Mostafa Jahanifar, Shan E Ahmed Raza, Fayyaz Minhas, Abhir Bhalerao, Nasir Rajpoot

Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data.

Classification Nuclear Segmentation +1

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

1 code implementation28 Feb 2022 Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Shan E Ahmed Raza, Fayyaz Minhas, David Snead, Nasir Rajpoot

In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources.

Cell Detection Explainable Models +4

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

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 Segmentation +1

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

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

4 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

8 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 +2

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.

Clustering Instance Segmentation +3

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 Image-Variation

Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images

1 code implementation28 Feb 2017 Mostafa Jahanifar, Neda Zamani Tajeddin, Babak Mohammadzadeh Asl, Ali Gooya

In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI).

Lesion Segmentation Saliency Detection +1

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