162 citations · 166 across the 8 of their papers we have counts for
10 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…
Tera-MIND: Tera-scale mouse brain simulation via spatial mRNA-guided diffusion
Jiqing Wu, Ingrid Berg, Yawei Li +2
Holistic 3D modeling of molecularly defined brain structures is crucial for understanding complex brain functions. Using emerging tissue profiling technologies, researchers charted…
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…
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…