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Found 10 papers, 4 papers with code
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Vision foundation models (FMs) are accelerating the development of digital pathology algorithms and transforming biomedical research.
SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction
We find that a "soft", probability-map instance segmentation ground truth leads to best model performance.
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains.
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models
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) model generalizability.
Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides
Automating tissue segmentation and tumor detection in histopathology images of colorectal cancer (CRC) is an enabler for faster diagnostic pathology workflows.
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset
Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task.
Mitosis domain generalization in histopathology images -- The MIDOG challenge
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
Towards IID representation learning and its application on biomedical data
Compared to the SOTA baselines supported in WILDS, the results confirm the superior performance of IID representation learning on OOD tasks.
Automated causal inference in application to randomized controlled clinical trials
Randomized controlled trials (RCTs) are considered as the gold standard for testing causal hypotheses in the clinical domain.
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge
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 the MIDOG 2021 competition.
