J. Laak

发表

A survey on deep learning in medical image analysis

Bram van Ginneken, Geert J. S. Litjens, Clara I. Sánchez, 2017, Medical Image Anal..

Residual cyclegan for robust domain transformation of histopathological tissue slides

Jeroen van der Laak, Thomas de Bel, Geert Litjens, 2021, Medical Image Anal..

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

Jeroen van der Laak, Francesco Ciompi, Maschenka Balkenhol, 2020, Medical Image Anal..

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Geert J. S. Litjens, Jeroen van der Laak, Péter Bándi, 2019, Medical Image Anal..

Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology

Geert J. S. Litjens, Jeroen A. W. M. van der Laak, Thomas de Bel, 2018, MIDL.

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Nico Karssemeijer, Bram van Ginneken, Geert J. S. Litjens, 2017, Journal of medical imaging.

A New Fiji-Based Algorithm That Systematically Quantifies Nine Synaptic Parameters Provides Insights into Drosophila NMJ Morphometry

Bonnie Nijhof, Anna Castells-Nobau, Louis Wolf, 2016, PLoS Comput. Biol..

Learning to detect lymphocytes in immunohistochemistry with deep learning

Geert J. S. Litjens, Jeremy Parry, Jeroen van der Laak, 2019, Medical Image Anal..

Automated Gleason Grading of Prostate Biopsies using Deep Learning pdf

Bram van Ginneken, Jeroen van der Laak, Hester van Boven, 2019, ArXiv.

placenta retained cells in the basal decidua is decreased in The number of multinucleated trophoblastic giant

J. V. D. Laak, J. Bulten, F. Lotgering, 2009 .

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Manfredo Atzori, Jeroen van der Laak, Damian Podareanu, 2021, Frontiers in Computer Science.

Tailoring automated data augmentation to H&E-stained histopathology

Jeroen van der Laak, Khrystyna Faryna, Geert Litjens, 2021, MIDL.

The importance of stain normalization in colorectal tissue classification with convolutional networks

Bram van Ginneken, Geert J. S. Litjens, Jeroen van der Laak, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images

Nico Karssemeijer, Geert J. S. Litjens, Jeroen van der Laak, 2016, IEEE Transactions on Medical Imaging.

Deep learning enables fully automated mitotic density assessment in breast cancer histopathology

J. A. van der Laak, M. Balkenhol, P. Bult, 2020 .

A study into methodology and application of quantification of tumour vasculature in rectal cancer

I. Nagtegaal, C. V. D. Velde, H. Krieken, 2004, Virchows Archiv.

Automated identification of cell and tissue components in pathology

J. V. D. Laak, J. Laak, 2001 .

Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

Jeroen van der Laak, Michal Drozdzal, Francesco Ciompi, 2020, MIDL.

High-resolution Image Registration of Consecutive and Re-stained Sections in Histopathology pdf

Stefan Heldmann, Jeroen van der Laak, Nick Weiss, 2021, ArXiv.

Comparison of consecutive and restained sections for image registration in histopathology

S. Heldmann, J. Lotz, Nick Weiss, 2021, Journal of medical imaging.

Digital and Computational Pathology: Bring the Future into Focus

Vijayan K. Asari, Vijayan K. Asari, Nikolaos Grammalidis, 2019, Journal of Pathology Informatics.

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard

Bram van Ginneken, Jeroen van der Laak, Johannes Lotz, 2018, Scientific Reports.

Domain adaptation strategies for cancer-independent detection of lymph node metastases

G. Litjens, M. Balkenhol, B. Ginneken, 2022, ArXiv.

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Navid Alemi Koohbanani, M. Veta, Shadi Albarqouni, 2023, ArXiv.

Multi-resolution deep learning characterizes tertiary lymphoid structures in solid tumors

H. Moch, F. Ciompi, P. Schraml, 2023, bioRxiv.

Quantitative analysis of stain variability in histology slides and an algorithm for standardization

Nico Karssemeijer, Jeroen van der Laak, Babak Ehteshami Bejnordi, 2014, Medical Imaging.

Histopathology stain-color normalization using deep generative models

Svitlana Zinger, Farhad Ghazvinian Zanjani, Jeroen A. W. M. van der Laak, 2018 .

H and E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection

Nico Karssemeijer, Geert J. S. Litjens, Jeroen van der Laak, 2018, Medical Imaging.

Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

Nico Karssemeijer, Andrew H. Beck, Jimmy Lin, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Predictive uncertainty estimation for out-of-distribution detection in digital pathology

G. Litjens, Stefan Elfwing, J. Linmans, 2022, Medical Image Anal..

Stain Specific Standardization of Whole-Slide Histopathological Images

Nico Karssemeijer, Geert J. S. Litjens, Jeroen van der Laak, 2016, IEEE Transactions on Medical Imaging.

Evaluation of tongue squamous cell carcinoma resection margins using ex-vivo MR

Geert J. S. Litjens, Jeroen van der Laak, Jurgen J. Fütterer, 2017, International Journal of Computer Assisted Radiology and Surgery.

Stain normalization of histopathology images using generative adversarial networks

Peter H. N. de With, Svitlana Zinger, Jeroen van der Laak, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Structure Instance Segmentation in Renal Tissue: A Case Study on Tubular Immune Cell Detection

Geert J. S. Litjens, Jeroen van der Laak, Thomas de Bel, 2018, COMPAY/OMIA@MICCAI.

A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images

Nico Karssemeijer, Geert J. S. Litjens, Jeroen van der Laak, 2015, Medical Imaging.

You Only Look on Lymphocytes Once

F. Ciompi, J. V. D. Laak, M. V. Rijthoven, 2018 .

Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning

Jeroen van der Laak, Francesco Ciompi, John-Melle Bokhorst, 2018, COMPAY/OMIA@MICCAI.

Comparison of different methods for tissue segmentation in histopathological whole-slide images

Bram van Ginneken, Geert J. S. Litjens, Jeroen van der Laak, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images

Geert J. S. Litjens, Jeroen A. W. M. van der Laak, Francesco Ciompi, 2018 .

Learning from sparsely annotated data for semantic segmentation in histopathology images

Jeroen A. W. M. van der Laak, Francesco Ciompi, Hans Pinckaers, 2018, MIDL.

Automatic segmentation of histopathological slides of renal tissue using deep learning

Geert J. S. Litjens, Bart Smeets, Jeroen van der Laak, 2018, Medical Imaging.

Towards Spatial Correspondence between Specimen and In-vivo Breast Imaging

Nico Karssemeijer, David J. Hawkes, John H. Hipwell, 2014, Digital Mammography / IWDM.

Continual learning strategies for cancer-independent detection of lymph node metastases

B. van Ginneken, G. Litjens, M. Balkenhol, 2023, Medical Image Anal..

Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks

Geert J. S. Litjens, Jasper Linmans, Jeroen A. W. M. van der Laak, 2020, MIDL.

Variational Weakly Supervised Gaussian Processes

Melih Kandemir, Fred A. Hamprecht, Jeroen van der Laak, 2016, BMVC.

Automatic color unmixing of IHC stained whole slide images

Geert J. S. Litjens, Jeroen van der Laak, Milad Intezar, 2018, Medical Imaging.

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

G. Litjens, W. Bulten, F. Ciompi, 2019, Medical Image Anal..

A survey on deep learning in medical image analysis

G. Litjens, B. Ginneken, Thijs Kooi, 2017, Medical Image Anal..

Automated Gleason Grading of Prostate Biopsies using Deep Learning

Thomas de Bel, G. Litjens, B. Ginneken, 2019, ArXiv.

University of Birmingham Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer

Rajarsi R. Gupta, J. D. Hansen, N. Halama, 2023 .

Gigapixel end-to-end training using streaming and attention

G. Litjens, H. Pinckaers, K. Hebeda, 2023, Medical Image Anal..

Editorial Computational Pathology

Mitko Veta, Nasir M. Rajpoot, Jeroen van der Laak, 2021, IEEE journal of biomedical and health informatics.

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Navid Alemi Koohbanani, M. Veta, Shadi Albarqouni, 2023, IEEE Journal of Biomedical and Health Informatics.

Uncertainty-guided annotation enhances segmentation with the human-in-the-loop

G. Litjens, F. Ciompi, Joey Spronck, 2024, 2404.07208.

Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology

G. Litjens, Khrystyna Faryna, J. Laak, 2024, Comput. Biol. Medicine.

Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images

Witali Aswolinskiy, Francesco Ciompi, Jeroen van der Laak, 2021, Medical Imaging.

Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer pdf

Hugo M. Horlings, Jeroen van der Laak, Carsten Denkert, 2020, ArXiv.

nnUNet meets pathology: Bridging the gap for application to whole slide images and computational biomarkers

Lieke van der Woude, Mart van Rijthoven, F. Ciompi, 2023, MIDL.