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Found 43 papers, 19 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.
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.
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 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.
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation
We find that the efficiency of this method highly depends on the choice of AL step size (i. e., the combination of region size and the number of selected regions per WSI), and a suboptimal AL step size can result in redundant annotation requests or inflated computation costs.
Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models
This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
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.
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.
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.
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity.
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.
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.
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge
This work presents a mitotic figure detection algorithm developed as a baseline for the challenge, based on domain adversarial training.
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.
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.
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
CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding
To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding.
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.
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage.
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.
Motion Artifact Detection in Confocal Laser Endomicroscopy Images
Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing non-invasive examination of the mucosa on a (sub)cellular level, has proven to be a valuable diagnostic tool in gastroenterology and shows promising results in various anatomical regions including the oral cavity.
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.
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
We find that the network trained on the oral cavity data reaches an accuracy of 89. 45% and an area-under-the-curve (AUC) value of 0. 955, when applied on the vocal cords data.
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion.
