3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS)

Autor: Manon Scholaert, Raissa Houmadi, Jeremy Martin, Nadine Serhan, Marie Tauber, Emilie Braun, Lilian Basso, Eric Merle, Pascal Descargues, Manuelle Viguier, Cécile Lesort, Benoît Chaput, Jean Kanitakis, Denis Jullien, Cristina Bulai Livideanu, Laurence Lamant, Emeline Pagès, Nicolas Gaudenzio

Routine clinical assays, such as conventional immunohistochemistry, often fail to resolve the regional heterogeneity of complex inflammatory skin conditions. Here we introduce MANTIS (Multiplexed Annotated Tissue Imaging System), a flexible analytic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2652fbff762f30af57f89a2013cad20b
https://doi.org/10.1101/2023.01.13.523748

3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS).

Publikováno v: NewsRx COVID-19 Weekly; 1/30/2023, p26-26, 1p

Structured crowdsourcing enables convolutional segmentation of histology images

Autor: Mustafijur Rahman, Hazem S. E. Salem, Nada M. Elgazar, Anas M. Saad, Rokia Adel Sakr, Abo-Alela F. Younes, Mohamed Amgad, Mariam M. Khalaf, Inas A. Ruhban, Ahmad M. Elkashash, Ali Abdulkarim, Habiba Elfandy, Yahya Alagha, David E. Manthey, Ahmed F. Ismail, Mohamed Hosny Osman, David A. Gutman, Hagar Hussein, Duaa M. Younes, Ahmed M. Alhusseiny, Ahmed Gadallah, Lamees A. Atteya, Mai A. T. Elsebaie, Joumana Ahmed, Jonathan D. Beezley, Deepak Roy Chittajallu, Basma M. Zaki, Lee Cooper, Lamia S. Abo Elnasr, Maha A. T. Elsebaie, Salma Y. Fala

Publikováno v: Bioinformatics

Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5076a699604d04a8ca3562245785c1ec
http://europepmc.org/articles/PMC6748796

Learning from crowds in digital pathology using scalable variational Gaussian processes

Autor: Rafael Molina, Lee Cooper, Pablo Morales-Alvarez, Mohamed Amgad, Aggelos K. Katsaggelos, Miguel López-Pérez, Pablo Ruiz

Publikováno v: Scientific Reports
Digibug. Repositorio Institucional de la Universidad de Granada
instname
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)

This work was supported by the Agencia Estatal de Investigacion of the Spanish Ministerio de Ciencia e Innovacion under contract PID2019-105142RB-C22/AEI/10.13039/501100011033, and the United States National Institutes of Health National Cancer Insti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::496946b972219952c83b5e6d5414ef19
http://hdl.handle.net/10481/70399

HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification

Autor: Lauren Alisha Fernandes, Maria Gabrani, Maria Frucci, Gerardo Botti, Giuseppe De Pietro, Florinda Feroce, Maurizio Di Bonito, Daniel Riccio, Jean-Philippe Thiran, Pushpak Pati, Orcun Goksel, Antonio Foncubierta-Rodríguez, Anna Maria Anniciello, Guillaume Jaume, Nadia Brancati, Giosuè Scognamiglio

Publikováno v: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis ISBN: 9783030603649
UNSURE/GRAIL@MICCAI
GRAIL 2020, in conjunction with MICCAI 2020, pp. 208–219, Lima, Perù, 08/10/2020
info:cnr-pdr/source/autori:P. Pati, G. Jaume, L. Alisha Fernandes, A. Foncubierta, F. Feroce, A. M. Anniciello, G. Scognamiglio, N. Brancati, D.Riccio, M. Di Bonito, G. De Pietro, G. Botti, O. Goksel, J. P. Thiran, M. Frucci, M. Gabrani/congresso_nome:GRAIL 2020, in conjunction with MICCAI 2020,/congresso_luogo:Lima, Perù/congresso_data:08%2F10%2F2020/anno:2020/pagina_da:208/pagina_a:219/intervallo_pagine:208–219

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5502452d497b9f142cd325f97e3d2795
https://doi.org/10.1007/978-3-030-60365-6_20