M. Balkenhol

发表

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Andrew H. Beck, Quirine F. Manson, Oren Z. Kraus, 2017, JAMA.

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

Francesco Ciompi, David Tellez, Peter Bult, 2019, Cellular Oncology.

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

Martin Eklund, Paulo Salles, Guilherme Pereira, 2020, Modern Pathology.

Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound.

Nico Karssemeijer, Tao Tan, Ritse M Mann, 2017, Ultrasound in medicine & biology.

Deep learning assisted mitotic counting for breast cancer

Francesco Ciompi, Hans Pinckaers, David Tellez, 2019, Laboratory Investigation.

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..

Few-shot weakly supervised detection and retrieval in histopathology whole-slide images

Manfredo Atzori, Francesco Ciompi, Jeroen van der Laak, 2021, Medical Imaging.

Evaluation Criteria for Chromosome Instability Detection by FISH to Predict Malignant Progression in Premalignant Glottic Laryngeal Lesions

J. A. van der Laak, M. Balkenhol, M. V. van Velthuysen, 2022, Cancers.

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

Brett Hemamali David J. Andrew J. Daniel M. Chin-Chen Gl Delahunt Samaratunga Grignon Evans Berney P, G. Litjens, J. A. van der Laak, 2020, Modern Pathology.

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

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

Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer

J. A. van der Laak, M. Balkenhol, P. Bult, 2022, npj Breast Cancer.

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

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

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies

J. A. van der Laak, M. Balkenhol, F. Ciompi, 2022, medRxiv.

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

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

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.

Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics

G. Litjens, J. A. van der Laak, M. Balkenhol, 2021, Breast.

Learning to detect lymphocytes in immunohistochemistry with deep learning

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

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Shaoqun Zeng, Jörg Franke, Fabian Both, 2019, IEEE Transactions on Medical Imaging.

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Nico Karssemeijer, Francesco Ciompi, Jeroen van der Laak, 2018, IEEE Transactions on Medical Imaging.

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset

Meyke Hermsen, Alexi Baidoshvili, Nikolas Stathonikos, 2018, GigaScience.

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 and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

J. A. van der Laak, M. Balkenhol, P. Bult, 2019, Cellular Oncology.

Interobserver variability in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative invasive breast carcinoma influences the association with pathological complete response: the IVITA study

M. Balkenhol, S. Fox, A. Vincent-Salomon, 2021, Modern Pathology.

Histological subtypes in triple negative breast cancer are associated with specific information on survival.

J. A. van der Laak, M. Balkenhol, P. Bult, 2020, Annals of Diagnostic Pathology.

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.

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

Bram van Ginneken, Péter Bándi, Jeroen van der Laak, 2019, PeerJ.

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 .

Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer

Krishna R. Kalari, F. Couch, M. Balkenhol, 2024, NPJ breast cancer.

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.