C. Matek

发表

Built to last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology

Sayedali Shetab Boushehri, Dominik Jens Elias Waibel, C. Marr, 2022, medRxiv.

More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation

C. Matek, 2022, Patterns.

The temperature dependence of the helical twist of DNA

M. Zacharias, Tomáš Dršata, F. Lankaš, 2018, Nucleic acids research.

Plectoneme tip bubbles: Coupled denaturation and writhing in supercoiled DNA

J. Doye, A. Louis, T. Ouldridge, 2014, Scientific Reports.

Long-range correlations in the mechanics of small DNA circles under topological stress revealed by multi-scale simulation

A. Louis, C. Benham, C. Laughton, 2016, Nucleic acids research.

Data Models for Dataset Drift Controls in Machine Learning With Images

Roderick Murray-Smith, W. Samek, E. Pomarico, 2022, ArXiv.

Statistical mechanics of nucleic acids under mechanical stress

Christian Matek, C. Matek, Christian Matek, 2014 .

Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks

K. Spiekermann, C. Marr, Christian Matek, 2019, Nat. Mach. Intell..

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set

Christian Münzenmayer, Carsten Marr, Sebastian Krappe, 2021, Blood.

Coarse-graining DNA for simulations of DNA nanotechnology.

Lorenzo Rovigatti, Flavio Romano, Christian Matek, 2013, Physical chemistry chemical physics : PCCP.

Make deep learning algorithms in computational pathology more reproducible and reusable

Dominik Jens Elias Waibel, M. Boxberg, C. Matek, 2022, Nature Medicine.

Explainable AI identifies diagnostic cells of genetic AML subtypes

T. Haferlach, K. Spiekermann, C. Marr, 2023, PLOS digital health.

Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks

K. Spiekermann, C. Marr, Christian Matek, 2019, Nature Machine Intelligence.

Tens of images can suffice to train neural networks for malignant leukocyte detection

C. Marr, D. Bosnacki, C. Matek, 2021, Scientific Reports.

Data Models for Dataset Drift Controls in Machine Learning With Images

Roderick Murray-Smith, W. Samek, E. Pomarico, 2022, ArXiv.

Coarse-grained modelling of supercoiled RNA.

Christian Matek, Petr Šulc, Jonathan P K Doye, 2015, The Journal of chemical physics.

Searching for a conceptual language in Systems Biology: Hints from Statistical Mechanics?

Christian Matek, C. Matek, 2013, Progress in biophysics and molecular biology.

DNA cruciform arms nucleate through a correlated but asynchronous cooperative mechanism.

Christian Matek, Jonathan P K Doye, Thomas E Ouldridge, 2012, The journal of physical chemistry. B.

ML4H Auditing: From Paper to Practice

Suproteem K. Sarkar, Matthew B. A. McDermott, Ferath Kherif, 2020, ML4H@NeurIPS.

Tumor–Stroma Ratio in Colorectal Cancer—Comparison between Human Estimation and Automated Assessment

Christian Matek, S. Merkel, P. Kuritcyn, 2023, Cancers.

Data Models for Dataset Drift Controls in Machine Learning With Optical Images

Roderick Murray-Smith, W. Samek, E. Pomarico, 2022, Trans. Mach. Learn. Res..

A deep-learning workflow to predict upper tract urothelial cancer subtypes supporting the prioritization of patients for molecular testing

R. Stoehr, H. Taubert, S. Wach, 2023, medRxiv.

Machine Learning for Health: Algorithm Auditing & Quality Control

F. Cabitza, W. Samek, Ferath Kherif, 2021, Journal of Medical Systems.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Jakob Nikolas Kather, P. A. van den Brandt, H. Grabsch, 2023, Cancer cell.

Proposal for a Novel Histological Scoring System as a Potential Grading Approach for Muscle-invasive Urothelial Bladder Cancer Correlating with Disease Aggressiveness and Patient Outcomes.

M. Amin, R. Montironi, G. Netto, 2023, European urology oncology.