FedCV
Brain-Tumor-Segmentation-And-Classification
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| FedCV | Brain-Tumor-Segmentation-And-Classification | |
|---|---|---|
| 2 | 3 | |
| 62 | 18 | |
| - | - | |
| 2.7 | 3.0 | |
| over 1 year ago | 9 months ago | |
| Python | Python | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
FedCV
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[R] AI Researchers Propose An Easy-To-Use Federated Learning Framework Called ‘FedCV’ For Diverse Computer Vision Tasks
Code for https://arxiv.org/abs/2111.11066 found: https://github.com/FedML-AI/FedCV
Brain-Tumor-Segmentation-And-Classification
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Brain Tumor Segmentation and Classification using ResUnet.
Source code: Github
- Github project showcase: Brain Tumor Segmentation and Classification using ResUnet.
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Brain Tumor Segmentation and Classification using deep learning.
GitHub repo : Brain Tumor Segmentation and Classification
What are some alternatives?
dipy - DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
MONAILabel - MONAI Label is an intelligent open source image labeling and learning tool.
medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125