Search Results for author:
Found 11 papers, 4 papers with code
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.
Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides
This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis.
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.
Nuclear Pleomorphism in Canine Cutaneous Mast Cell Tumors: Comparison of Reproducibility and Prognostic Relevance between Estimates, Manual Morphometry and Algorithmic Morphometry
We assessed the following nuclear evaluation methods for measurement accuracy, reproducibility, and prognostic utility: 1) anisokaryosis (karyomegaly) estimates by 11 pathologists; 2) gold standard manual morphometry of at least 100 nuclei; 3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and 4) automated morphometry using a deep learning-based segmentation algorithm.
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 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.
Automatic and explainable grading of meningiomas from histopathology images
In a first approach, image patches are sampled from this region and regression is based on morphological features encoded by a ResNet-based network.
