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Found 13 papers, 5 papers with code
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation
Atypical mitoses mark a deviation in the cell division process that can be an independent prognostically relevant marker for tumor malignancy.
Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer.
Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification
Foundation models (FMs), i. e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology.
On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images
Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E.
Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis
The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources.
Re-identification from histopathology images
We predicted the source patient of a slide with F1 scores of 50. 16 % and 52. 30 % on the LSCC and LUAD datasets, respectively, and with 62. 31 % on our meningioma dataset.
Rethinking U-net Skip Connections for Biomedical Image Segmentation
Previous works showed that the trained network layers differ in their susceptibility to this domain shift, e. g., shallow layers are more affected than deeper layers.
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.
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections.
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.
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.
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays
Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems.
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.
