Maxime W. Lafarge

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Learning Domain-Invariant Representations of Histological Images

Mitko Veta, Josien P. W. Pluim, Koen A. J. Eppenhof, 2019, Front. Med..

Training deep convolutional neural networks for deformable image registration of pulmonary CT images

Josien P. W. Pluim, Koen A. J. Eppenhof, Maxime W. Lafarge, 2018 .

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support pdf

Jaime S. Cardoso, Mitko Veta, Josien P. W. Pluim, 2017, Lecture Notes in Computer Science.

Inferring a third spatial dimension from 2D histological images

Mitko Veta, Josien P. W. Pluim, Pim Moeskops, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology pdf

Josien P.W. Pluim, Maxime W. Lafarge, Mitko Veta, 2020, ArXiv.

Roto-Translation Covariant Convolutional Networks for Medical Image Analysis pdf

Mitko Veta, Josien P. W. Pluim, Remco Duits, 2018, MICCAI.

Progressively growing convolutional networks for end-to-end deformable image registration

Josien P. W. Pluim, Koen A. J. Eppenhof, Maxime W. Lafarge, 2019, Medical Imaging: Image Processing.

Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

Remco Duits, Erik J. Bekkers, Josien P.W. Pluim, 2020, Medical Image Anal..

Deformable image registration using convolutional neural networks

Mitko Veta, Josien P. W. Pluim, Pim Moeskops, 2018, Medical Imaging.

Adversarial Training and Dilated Convolutions for Brain MRI Segmentation

Mitko Veta, Josien P. W. Pluim, Pim Moeskops, 2017, DLMIA/ML-CDS@MICCAI.

Capturing Single-Cell Phenotypic Variation via Unsupervised Representation Learning

Mitko Veta, Josien P. W. Pluim, Juan C. Caicedo, 2019, MIDL.

Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge pdf

Viktor H. Koelzer, Maxime W. Lafarge, 2021, ArXiv.

Progressively Trained Convolutional Neural Networks for Deformable Image Registration

Josien P W Pluim, Koen A J Eppenhof, Maxime W. Lafarge, 2019, IEEE Transactions on Medical Imaging.

Towards computationally efficient prediction of molecular signatures from routine histology images.

Maxime W. Lafarge, V. Koelzer, 2021, The Lancet. Digital health.

Mitosis domain generalization in histopathology images - The MIDOG challenge

Maxime W. Lafarge, T. Wittenberg, M. Veta, 2022, Medical Image Anal..

Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow

Maxime W. Lafarge, P. V. van Diest, M. Veta, 2023, Journal of Pathology Informatics.

Towards IID representation learning and its application on biomedical data

Maxime W. Lafarge, Jiqing Wu, I. Zlobec, 2022, MIDL.

Multimodal angiographic assessment of cerebral arteriovenous malformations: a pilot study

Raphaël Blanc, Jérôme Hodel, Thomas Robert, 2014, Journal of NeuroInterventional Surgery.

Domain generalization across tumor types, laboratories, and species - insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Xiang 'Anthony' Chen, Maxime W. Lafarge, M. Veta, 2023, Medical Image Anal..

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models

Maxime W. Lafarge, A. Madabhushi, A. Janowczyk, 2024, Npj imaging.

CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models

Maxime W. Lafarge, A. Madabhushi, A. Janowczyk, 2023, ArXiv.

Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset

Maxime W. Lafarge, V. Koelzer, 2023, MIDOG/DRAC@MICCAI.

Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides

Maxime W. Lafarge, V. Koelzer, A. Frei, 2023, ArXiv.