Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
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Updated
Apr 29, 2024 - Python
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
Digital pathology image viewer with support for human/machine generated annotations and markups.
QuPath - Bioimage analysis & digital pathology
Bio-Formats is a Java library for reading and writing data in life sciences image file formats. It is developed by the Open Microscopy Environment. Bio-Formats is released under the GNU General Public License (GPL); commercial licenses are available from Glencoe Software.
Cancer metastasis detection with neural conditional random field (NCRF)
Program for the analysis and visualization of whole-slide images in digital pathology
Read and write TIFF files
DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
a browser-based DICOM viewer
Deep learning library for digital pathology, with both Tensorflow and PyTorch support.
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
Code for the paper " PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers "
Cytomine-Core is the main web server implementing the Cytomine API
A Python module that produces image patches and annotation masks from whole slide images for deep learning in digital pathology.
A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
An open source online platform for collaborative image labeling of almost everything
Simple library for preprocessing histopathological whole-slide images (WSI) into tiles (a.k.a. patches) towards deep learning
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