whole slide images
122 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
Use these libraries to find whole slide images models and implementations
PanNuke
LKS
CryoNuSeg
MITOS_WSI_CMC
Most implemented papers
PanNuke Dataset Extension, Insights and Baselines
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides.
Deep Learning for Identifying Metastatic Breast Cancer
3dimaging/DeepLearningCamelyon
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The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies.
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.
HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images
lyndonchan/hsn_v1
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ICCV 2019
In digital pathology, tissue slides are scanned into Whole Slide Images (WSI) and pathologists first screen for diagnostically-relevant Regions of Interest (ROIs) before reviewing them.
A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification.
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.
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.
Code-free development and deployment of deep segmentation models for digital pathology
AICAN-Research/FAST-Pathology
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Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions.
A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images
Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data.
Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.
