From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology

Authors: Nahhas, Omar S. M. El, van Treeck, Marko, Wölflein, Georg, Unger, Michaela, Ligero, Marta, Lenz, Tim, Wagner, Sophia J., Hewitt, Katherine J., Khader, Firas, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas

Subject Terms: Computer Science - Computer Vision and Pattern Recognition

Access URL: http://arxiv.org/abs/2312.10944

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Authors: Nahhas, Omar S. M. El, Loeffler, Chiara M. L., Carrero, Zunamys I., van Treeck, Marko, Kolbinger, Fiona R., Hewitt, Katherine J., Muti, Hannah S., Graziani, Mara, Zeng, Qinghe, Calderaro, Julien, Ortiz-Brüchle, Nadina, Yuan, Tanwei, Hoffmeister, Michael, Brenner, Hermann, Brobeil, Alexander, Reis-Filho, Jorge S., Kather, Jakob Nikolas

Subject Terms: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

Access URL: http://arxiv.org/abs/2304.05153

Test Time Transform Prediction for Open Set Histopathological Image Recognition

Authors: Galdran, Adrian, Hewitt, Katherine J., Ghaffari, Narmin L., Kather, Jakob N., Carneiro, Gustavo, Ballester, Miguel A. González

Subject Terms: Computer Science - Computer Vision and Pattern Recognition

Access URL: http://arxiv.org/abs/2206.10033

Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance

Authors: Niehues, Jan M., Müller-Franzes, Gustav, Schirris, Yoni, Wagner, Sophia Janine, Jendrusch, Michael, Kloor, Matthias, Pearson, Alexander T., Muti, Hannah Sophie, Hewitt, Katherine J., Veldhuizen, Gregory P., Zigutyte, Laura, Truhn, Daniel, Kather, Jakob Nikolas

Source: In Computers in Biology and Medicine June 2024 175

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.

Authors: El Nahhas, Omar S. M., van Treeck, Marko, Wölflein, Georg, Unger, Michaela, Ligero, Marta, Lenz, Tim, Wagner, Sophia J., Hewitt, Katherine J., Khader, Firas, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas

Source: Nature Protocols; Jan2025, Vol. 20 Issue 1, p293-316, 24p

Large language models as a diagnostic support tool in neuropathology.

Authors: Hewitt, Katherine J, Wiest, Isabella C, Carrero, Zunamys I, Bejan, Laura, Millner, Thomas O, Brandner, Sebastian, Kather, Jakob Nikolas

Source: Journal of Pathology: Clinical Research; Nov2024, Vol. 10 Issue 6, p1-7, 7p

Subject Terms: LANGUAGE models, CENTRAL nervous system, NEUROLOGICAL disorders, GLIOMAS, TUMORS

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Authors: El Nahhas, Omar S. M., Loeffler, Chiara M. L., Carrero, Zunamys I., van Treeck, Marko, Kolbinger, Fiona R., Hewitt, Katherine J., Muti, Hannah S., Graziani, Mara, Zeng, Qinghe, Calderaro, Julien, Ortiz-Brüchle, Nadina, Yuan, Tanwei, Hoffmeister, Michael, Brenner, Hermann, Brobeil, Alexander, Reis-Filho, Jorge S., Kather, Jakob Nikolas

Source: Nature Communications; 9/16/2024, Vol. 15 Issue 1, p1-13, 13p

Subject Terms: TUMOR markers, HOMOLOGOUS recombination, DEEP learning, BIOMARKERS, COLORECTAL cancer

Automated curation of large‐scale cancer histopathology image datasets using deep learning.

Authors: Hilgers, Lars, Ghaffari Laleh, Narmin, West, Nicholas P, Westwood, Alice, Hewitt, Katherine J, Quirke, Philip, Grabsch, Heike I, Carrero, Zunamys I, Matthaei, Emylou, Loeffler, Chiara M L, Brinker, Titus J, Yuan, Tanwei, Brenner, Hermann, Brobeil, Alexander, Hoffmeister, Michael, Kather, Jakob Nikolas

Source: Histopathology; Jun2024, Vol. 84 Issue 7, p1139-1153, 15p

Subject Terms: DEEP learning, ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, HISTOPATHOLOGY, ARTIFICIAL intelligence, COLORECTAL cancer

Geographic Terms: GERMANY, UNITED Kingdom

Extracting structured information from unstructured histopathology reports using generative pre‐trained transformer 4 (GPT‐4).

Authors: Truhn, Daniel, Loeffler, Chiara ML, Müller‐Franzes, Gustav, Nebelung, Sven, Hewitt, Katherine J, Brandner, Sebastian, Bressem, Keno K, Foersch, Sebastian, Kather, Jakob Nikolas

Source: Journal of Pathology; Mar2024, Vol. 262 Issue 3, p310-319, 10p

Subject Terms: LANGUAGE models, GENERATIVE pre-trained transformers, NATURAL language processing, MACHINE learning, HISTOPATHOLOGY

Company/Entity: JOHN Wiley & Sons Inc.

Authors' reply: Re: Koga et al. Retrieval‐augmented generation versus document‐grounded generation: a key distinction in large language models.

Authors: Hewitt, Katherine J, Wiest, Isabella C, Kather, Jakob N

Source: Journal of Pathology: Clinical Research; Jan2025, Vol. 11 Issue 1, p1-2, 2p

Subject Terms: LANGUAGE models, DIAGNOSIS, GLIOMAS, NATURAL language processing, RESEARCH methodology

Direct image to subtype prediction for brain tumors using deep learning.

Authors: Hewitt, Katherine J, Löffler, Chiara M L, Muti, Hannah Sophie, Berghoff, Anna Sophie, Eisenlöffel, Christian, Treeck, Marko van, Carrero, Zunamys I, Nahhas, Omar S M El, Veldhuizen, Gregory P, Weil, Sophie, Saldanha, Oliver Lester, Bejan, Laura, Millner, Thomas O, Brandner, Sebastian, Brückmann, Sascha, Kather, Jakob Nikolas

Source: Neuro-Oncology Advances; 2023, Vol. 5 Issue 1, p1-11, 11p

Author Correction: Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Authors: El Nahhas, Omar S. M., Loeffler, Chiara M. L., Carrero, Zunamys I., van Treeck, Marko, Kolbinger, Fiona R., Hewitt, Katherine J., Muti, Hannah S., Graziani, Mara, Zeng, Qinghe, Calderaro, Julien, Ortiz-Brüchle, Nadina, Yuan, Tanwei, Hoffmeister, Michael, Brenner, Hermann, Brobeil, Alexander, Reis-Filho, Jorge S., Kather, Jakob Nikolas

Source: Nature Communications; 2/29/2024, Vol. 15 Issue 1, p1-1, 1p

Subject Terms: MOLECULAR pathology, BIOMARKERS, PATHOLOGY, APPLIED sciences