Výsledky vyhledávání - "Guillaume Jaume"

Graph representation learning & explainability in breast cancer pathology: bridging the gap between AI and pathology practice

Autor: Pushpak Pati, Guillaume Jaume, Antonio Foncubierta-Rodriguez, Florinda Feroce, Giosue Scognamiglio, Anna Maria Anniciello, Nadia Brancati, Maria Frucci, Daniel Riccio, Jean-Philippe Thiran, Orcun Goksel, Maria Gabrani

Publikováno v: Artificial Intelligence Applications in Human Pathology, edited by Huss, Ralf; Grunkin, Michael., pp. 243–285. Londra: World Scientific Publishing, 2022
info:cnr-pdr/source/autori:P. Pati, G. Jaume, A. Foncubierta-Rodriguez, F. Feroce, G. Scognamiglio, A. M. Anniciello, N. Brancati, M. Frucci, D. Riccio, J.-P. Thiran, O. Goksel, M. Gabrani/titolo:Graph representation learning & explainability in breast cancer pathology: bridging the gap between AI and pathology practice/titolo_volume:Artificial Intelligence Applications in Human Pathology/curatori_volume:Huss, Ralf; Grunkin, Michael./editore: /anno:2022

While cancer cases continue to increase and diagnosis, prognosis and treatment become more digital, AI-assisted cancer patient care, in particular in the pathology daily practice, remains scarce and rudimentary. In this chapter, we focus on reducing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2dc3510f7f563ed4b649c99b95190fbd
https://publications.cnr.it/doc/463064

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Learning Whole-Slide Segmentation from Inexact and Incomplete Labels Using Tissue Graphs

Autor: Pushpak Pati, Mathilde Sibony, Jean-Philippe Thiran, Orcun Goksel, Valentin Anklin, Guillaume Jaume, Behzad Bozorgtabar, Maria Gabrani, Antonio Foncubierta-Rodríguez

Publikováno v: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871956
MICCAI (2)

Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant regions in scanned histology slid

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f4156fe3e80e7d6f7c92aae84c414c0b
https://doi.org/10.1007/978-3-030-87196-3_59

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BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

Autor: Nadia Brancati, Anna Maria Anniciello, Pushpak Pati, Daniel Riccio, Giosuè Scognamiglio, Guillaume Jaume, Giuseppe De Pietro, Maurizio Di Bonito, Antonio Foncubierta, Gerardo Botti, Maria Gabrani, Florinda Feroce, Maria Frucci

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f6382ad1309567a9ce03bbd4148caf0

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Quantifying Explainers of Graph Neural Networks in Computational Pathology

Autor: Antonio Foncubierta, Florinda Feroce, Jean-Philippe Thiran, Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Tilman T. Rau, Anna Maria Anniciello, Orcun Goksel, Maria Gabrani

Publikováno v: CVPR

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological en

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::937bcd483d0e2fadcfdef2eef9d9034b
http://arxiv.org/abs/2011.12646

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Hierarchical graph representations in digital pathology

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

Publikováno v: Medical Image Analysis, 75
Medical image analysis
75 (2022). doi:10.1016/j.media.2021.102264
info:cnr-pdr/source/autori:P. Pati, G. Jaume, A. Foncubierta, F. Feroce, A. M. Anniciello, G. Scognamiglio, N. Brancati, M.Fiche, E. Dubruc, D.Riccio, M. Di Bonito, G. De Pietro, G. Botti, J. P. Thiran, M. Frucci, O. Goksel, M. Gabrani/titolo:Hierarchical Graph Representations in Digital Pathology/doi:10.1016%2Fj.media.2021.102264/rivista:Medical image analysis (Print)/anno:2022/pagina_da:/pagina_a:/intervallo_pagine:/volume:75

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological ent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::945b138e29ccc1a7248502c97c7b2204
https://doi.org/10.1016/j.media.2021.102264

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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

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Interpreting Data from Scanned Tables

Autor: Waleed Farrukh, Maria Gabrani, Antonio Foncubierta-Rodríguez, Orcun Goksel, Costas Bejas, Guillaume Jaume, Anca-Nicoleta Ciubotaru

Publikováno v: GREC@ICDAR

Densely-packed but structured scientific data are typically presented in the form of tables, which often appear in raster image form. To interpret data from scanned tables, understanding their hierarchical structure is vital. To further address the v

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bc6885f86d3fe75258f8be32e33e65de
https://doi.org/10.1109/icdar.2017.250

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FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

Autor: Hazim Kemal Ekenel, Jean-Philippe Thiran, Guillaume Jaume

Publikováno v: OST@ICDAR

We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae24920ae5b8c9d091aaa6724cd8d172
https://infoscience.epfl.ch/record/276543

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