162 citations · 166 across the 8 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…
SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction
Lydia A. Schoenpflug, Viktor H. Koelzer
Detecting and classifying cells in histopathology H\&E stained whole-slide images is a core task in computational pathology, as it provides valuable insight into the tumor microenv…
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…