162 citations · 195 across the 11 of their papers we have counts for
13 papers
Sequential Hard Mining: a data-centric approach for Mitosis Detection
Maxime W. Lafarge, Viktor H. Koelzer
With a continuously growing availability of annotated datasets of mitotic figures in histology images, finding the best way to optimally use with this unprecedented amount of data…
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan +27
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with…
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models
Fan Fan, Georgia Martinez, Thomas Desilvio +10
Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML)…
Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides
Lydia A. Schoenpflug, Maxime W. Lafarge, Anja L. Frei +1
Automating tissue segmentation and tumor detection in histopathology images of colorectal cancer (CRC) is an enabler for faster diagnostic pathology workflows. At the same time it…
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset
Maxime W. Lafarge, Viktor H. Koelzer
Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis…
Mitosis domain generalization in histopathology images -- The MIDOG challenge
Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram +32
The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitoti…
Towards IID representation learning and its application on biomedical data
Jiqing Wu, Inti Zlobec, Maxime Lafarge +2
Due to the heterogeneity of real-world data, the widely accepted independent and identically distributed (IID) assumption has been criticized in recent studies on causality. In thi…
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge
Maxime W. Lafarge, Viktor H. Koelzer
Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in…
Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology
Maxime W. Lafarge, Josien P. W. Pluim, Mitko Veta
Unsupervised learning enables modeling complex images without the need for annotations. The representation learned by such models can facilitate any subsequent analysis of large im…
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
Maxime W. Lafarge, Erik J. Bekkers, Josien P. W. Pluim +2
Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework t…
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
Erik J Bekkers, Maxime W Lafarge, Mitko Veta +3
We propose a framework for rotation and translation covariant deep learning using group convolutions. The group product of the special Euclidean motion group descri…
Inferring a Third Spatial Dimension from 2D Histological Images
Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof +2
Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast. This process results in 2D images of the specim…
Adversarial training and dilated convolutions for brain MRI segmentation
Pim Moeskops, Mitko Veta, Maxime W. Lafarge +2
Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversa…