whole slide images
195 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in whole slide images
Libraries
Use these libraries to find whole slide images models and implementations
PanNuke
CryoNuSeg
LKS
MITOS_WSI_CMC
Most implemented papers
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.
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
We propose fusing both modalities using a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens.
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories.
Attention to detail: inter-resolution knowledge distillation
The development of computer vision solutions for gigapixel images in digital pathology is hampered by significant computational limitations due to the large size of whole slide images.
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images.
Learning to Segment Breast Biopsy Whole Slide Images
We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels.
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.
Cancer Metastasis Detection With Neural Conditional Random Field
Compared to the baseline method without considering spatial correlations, we show that the proposed NCRF framework obtains probability maps of patch predictions with better visual quality.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
ncoudray/DeepPATH
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Nature Medicine 2018
In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue.
Fast GPU-Enabled Color Normalization for Digital Pathology
MEDAL-IITB/Fast_WSI_Color_Norm
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Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology.
