Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
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Updated
Jul 31, 2024 - Python
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
QuPath - Open-source bioimage analysis for research
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
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.
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.
DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
Powerful, open-source AI tools for digital pathology.
Digital pathology image viewer with support for human/machine generated annotations and markups.
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
a browser-based DICOM viewer
Code for the paper " PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers "
Code for paper: DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification [ECCV 2024]
A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
A Python module that produces image patches and annotation masks from whole slide images for deep learning in digital pathology.
Cytomine-Core is the main web server implementing the Cytomine API
Implementation of Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification approach.
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