162 citations · 195 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…