Abnormality-Driven Representation Learning for Radiology Imaging

Authors: Ligero, Marta, Lenz, Tim, Wölflein, Georg, Nahhas, Omar S. M. El, Truhn, Daniel, Kather, Jakob Nikolas

Subject Terms: Computer Science - Computer Vision and Pattern Recognition

Access URL: http://arxiv.org/abs/2411.16803

Benchmarking foundation models as feature extractors for weakly-supervised computational pathology

Authors: Neidlinger, Peter, Nahhas, Omar S. M. El, Muti, Hannah Sophie, Lenz, Tim, Hoffmeister, Michael, Brenner, Hermann, van Treeck, Marko, Langer, Rupert, Dislich, Bastian, Behrens, Hans Michael, Röcken, Christoph, Foersch, Sebastian, Truhn, Daniel, Marra, Antonio, Saldanha, Oliver Lester, Kather, Jakob Nikolas

Subject Terms: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition

Access URL: http://arxiv.org/abs/2408.15823

Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization

Authors: Khader, Firas, Nahhas, Omar S. M. El, Han, Tianyu, Müller-Franzes, Gustav, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel

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

Access URL: http://arxiv.org/abs/2406.01314

Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology

Authors: Ferber, Dyke, Nahhas, Omar S. M. El, Wölflein, Georg, Wiest, Isabella C., Clusmann, Jan, Leßman, Marie-Elisabeth, Foersch, Sebastian, Lammert, Jacqueline, Tschochohei, Maximilian, Jäger, Dirk, Salto-Tellez, Manuel, Schultz, Nikolaus, Truhn, Daniel, Kather, Jakob Nikolas

Subject Terms: Computer Science - Artificial Intelligence, Quantitative Biology - Tissues and Organs

Access URL: http://arxiv.org/abs/2404.04667

In-context learning enables multimodal large language models to classify cancer pathology images

Authors: Ferber, Dyke, Wölflein, Georg, Wiest, Isabella C., Ligero, Marta, Sainath, Srividhya, Laleh, Narmin Ghaffari, Nahhas, Omar S. M. El, Müller-Franzes, Gustav, Jäger, Dirk, Truhn, Daniel, Kather, Jakob Nikolas

Subject Terms: Computer Science - Computer Vision and Pattern Recognition

Access URL: http://arxiv.org/abs/2403.07407

Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology

Authors: Lenz, Tim, Nahhas, Omar S. M. El, Ligero, Marta, Kather, Jakob Nikolas

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

Access URL: http://arxiv.org/abs/2403.04558

Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology

Authors: Nahhas, Omar S. M. El, Wölflein, Georg, Ligero, Marta, Lenz, Tim, van Treeck, Marko, Khader, Firas, Truhn, Daniel, Kather, Jakob Nikolas

Subject Terms: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning

Access URL: http://arxiv.org/abs/2403.03891

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

Benchmarking Pathology Feature Extractors for Whole Slide Image Classification

Authors: Wölflein, Georg, Ferber, Dyke, Meneghetti, Asier R., Nahhas, Omar S. M. El, Truhn, Daniel, Carrero, Zunamys I., Harrison, David J., Arandjelović, Ognjen, Kather, Jakob Nikolas

Subject Terms: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning

Access URL: http://arxiv.org/abs/2311.11772

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

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