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