Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

Autor: Chiara Maria Lavinia Loeffler, Nadine T. Gaisa, Hannah Sophie Muti, Marko van Treeck, Amelie Echle, Narmin Ghaffari Laleh, Christian Trautwein, Lara R. Heij, Heike I. Grabsch, Nadina Ortiz Bruechle, Jakob Nikolas Kather

Publikováno v: Frontiers in Genetics, Vol 12 (2022)

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically releva

Externí odkaz: https://doaj.org/article/4906266d913945f587bd672a29af4664

Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

Autor: Jakob Nikolas Kather, Marie Louise Malmstrøm, Tine Plato Kuhlmann, Nicholas P. West, I. Gögenur, Heike I. Grabsch, Narmin Ghaffari Laleh, Katarina Levic, Lara R. Heij, Susanne Eiholm, Oliver Lester Saldanha, Aurora Bono, Amelie Echle, Katerina Kouvidi, Titus J. Brinker, Philip Quirke, Scarlet Brockmoeller

Publikováno v: Journal of Pathology, 256(3), 269-281. Wiley
Brockmoeller, S, Echle, A, Ghaffari Laleh, N, Eiholm, S, Malmstrøm, M L, Plato Kuhlmann, T, Levic, K, Grabsch, H I, West, N P, Saldanha, O L, Kouvidi, K, Bono, A, Heij, L R, Brinker, T J, Gögenür, I, Quirke, P & Kather, J N 2022, ' Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer ', Journal of Pathology, vol. 256, no. 3, pp. 269-281 . https://doi.org/10.1002/path.5831

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5031929de99a2356fbc01ef55646b790
https://doi.org/10.1002/path.5831

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

Autor: Narmin Ghaffari Laleh, Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, Amelie Echle, Oliver Lester Saldanha, Faisal Mahmood, Ming Y. Lu, Christian Trautwein, Rupert Langer, Bastian Dislich, Roman D. Buelow, Heike Irmgard Grabsch, Hermann Brenner, Jenny Chang-Claude, Elizabeth Alwers, Titus J. Brinker, Firas Khader, Daniel Truhn, Nadine T. Gaisa, Peter Boor, Michael Hoffmeister, Volkmar Schulz, Jakob Nikolas Kather

Publikováno v: Ghaffari Laleh, Narmin; Muti, Hannah Sophie; Loeffler, Chiara Maria Lavinia; Echle, Amelie; Saldanha, Oliver Lester; Mahmood, Faisal; Lu, Ming Y; Trautwein, Christian; Langer, Rupert; Dislich, Bastian; Buelow, Roman D; Grabsch, Heike Irmgard; Brenner, Hermann; Chang-Claude, Jenny; Alwers, Elizabeth; Brinker, Titus J; Khader, Firas; Truhn, Daniel; Gaisa, Nadine T; Boor, Peter; ... (2022). Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical image analysis, 79, p. 102474. Elsevier 10.1016/j.media.2022.102474
Medical Image Analysis, 79:102474. Elsevier

Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c8439fc63c967f910426d4cd5ca2075
https://boris.unibe.ch/170129/1/1-s2.0-S1361841522001219-main.pdf

The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups

Autor: Céline N Heinz, Amelie Echle, Sebastian Foersch, Andrey Bychkov, Jakob Nikolas Kather

Publikováno v: Histopathology : journal of the British Division of the International Academy of Pathology 80(7), 1121-1127 (2022). doi:10.1111/his.14659

Histopathology : journal of the British Division of the International Academy of Pathology 80(7), 1121-1127 (2022). doi:10.1111/his.14659
Published by Wiley-Blackwell, Oxford [u.a.]

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62553a450259fce7580bab8915ca3e50

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

Autor: Marko van Treeck, Didem Cifci, Narmin Ghaffari Laleh, Oliver Lester Saldanha, Chiara M. L. Loeffler, Katherine J. Hewitt, Hannah Sophie Muti, Amelie Echle, Tobias Seibel, Tobias Paul Seraphin, Christian Trautwein, Sebastian Foersch, Tom Luedde, Daniel Truhn, Jakob Nikolas Kather

The interpretation of digitized histopathology images has been transformed thanks to artificial intelligence (AI). End-to-end AI algorithms can infer high-level features directly from raw image data, extending the capabilities of human experts. In pa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f8bb9bdb7182bae44396a284e3cea142
https://doi.org/10.1101/2021.12.19.473344