Search Results for author:
Found 17 papers, 6 papers with code
Analysing Diffusion Segmentation for Medical Images
However, there is a notable lack of analysis and discussions on the differences between diffusion segmentation and image generation, and thorough evaluations are missing that distinguish the improvements these architectures provide for segmentation in general from their benefit for diffusion segmentation specifically.
Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation
Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e. g., layout for segmentation.
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.
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.
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.
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.
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.
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks
We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology.
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.
Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification
We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis.
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.
