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Found 11 papers, 3 papers with code
Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard
The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas.
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.
Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors
Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark.
Systematic Review of Methods and Prognostic Value of Mitotic Activity. Part 2: Canine Tumors
The goal of this systematic review is to scholarly analyze the methods and prognostic relevance of histologically measuring mitotic activity in canine tumors.
Systematic Review of Methods and Prognostic Value of Mitotic Activity. Part 1: Feline Tumors
The goal of this systematic review is scholarly analysis of all available references on mitotic activity in feline tumors, and to provide an overview of the measuring methods and prognostic value.
Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images
Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription.
Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector
In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis.
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.
Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors
For this study, we created the first open source data-set with 19, 983 annotations of BiNC and 1, 416 annotations of MuNC in 32 histological whole slide images of ccMCT.
How Many Annotators Do We Need? -- A Study on the Influence of Inter-Observer Variability on the Reliability of Automatic Mitotic Figure Assessment
Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours.
A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
We achieved a mean F1-score of 0. 791 on the test set and of up to 0. 696 on a human breast cancer dataset.
