Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images
Multiple medical institutions collaboratively training a model using federated learning (FL)
has become a promising solution for maximizing the potential of data-driven models, yet the …
has become a promising solution for maximizing the potential of data-driven models, yet the …
Mitosis domain generalization in histopathology images—the MIDOG challenge
The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with
tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by …
tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by …
Multi-modality artificial intelligence in digital pathology
In common medical procedures, the time-consuming and expensive nature of obtaining test
results plagues doctors and patients. Digital pathology research allows using computational …
results plagues doctors and patients. Digital pathology research allows using computational …
Assessment of mitotic activity in breast cancer: revisited in the digital pathology era
The assessment of cell proliferation is a key morphological feature for diagnosing various
pathological lesions and predicting their clinical behaviour. Visual assessment of mitotic …
pathological lesions and predicting their clinical behaviour. Visual assessment of mitotic …
Fair federated medical image segmentation via client contribution estimation
How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …
investigated how to reward clients based on their contribution (collaboration fairness), and …
A comprehensive multi-domain dataset for mitotic figure detection
The prognostic value of mitotic figures in tumor tissue is well-established for many tumor
types and automating this task is of high research interest. However, especially deep …
types and automating this task is of high research interest. However, especially deep …
A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images
Mitosis counting of biopsies is an important biomarker for breast cancer patients, which
supports disease prognostication and treatment planning. Developing a robust mitotic cell …
supports disease prognostication and treatment planning. Developing a robust mitotic cell …
Augmenting pathologists with NaviPath: design and evaluation of a human-AI collaborative navigation system
Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-
resolution tumor images to search for pathology patterns of interest. However, existing AI …
resolution tumor images to search for pathology patterns of interest. However, existing AI …
International guidelines for veterinary tumor pathology: a call to action
DJ Meuten, FM Moore, TA Donovan… - Veterinary …, 2021 - journals.sagepub.com
Standardization of tumor assessment lays the foundation for validation of grading systems,
permits reproducibility of oncologic studies among investigators, and increases confidence …
permits reproducibility of oncologic studies among investigators, and increases confidence …
Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy
The mitotic count (MC) is an important histological parameter for prognostication of
malignant neoplasms. However, it has inter-and intraobserver discrepancies due to …
malignant neoplasms. However, it has inter-and intraobserver discrepancies due to …