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Found 25 papers, 13 papers with code
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.
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.
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset
Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in- and cross-domain.
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.
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology
Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably.
Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging.
First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain
Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort.
Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens
Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes.
Robust quad-tree based registration of whole slide images
The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology.
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.
Learning to be EXACT, Cell Detection for Asthma on Partially Annotated Whole Slide Images
We evaluated our pipeline in a cross-validation setup with a fixed training set using a dataset of six equine WSIs of which four are partially annotated and used for training, and two fully annotated WSI are used for validation and testing.
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.
Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels
To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms.
EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control
In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation.
Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides
However, a significant percentage of the deliberately introduced false labels was not identified by the experts.
Human-Computer Interaction Image and Video Processing
Learning New Tricks from Old Dogs -- Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment
We were able to show that domain adversarial training considerably improves accuracy when applying mitotic figure classification learned from the canine on the human data sets (up to +12. 8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78, 047 hemosiderophages.
Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes.
Field Of Interest Proposal for Augmented Mitotic Cell Count: Comparison of two Convolutional Networks
For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity.
Augmented Mitotic Cell Count using Field Of Interest Proposal
This map is in a second step used to construct a mitotic activity estimate.
SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images
It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated.
A Guided Spatial Transformer Network for Histology Cell Differentiation
The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image.
