Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Niftynet | 1,170 | 4 years ago | 98 | apache-2.0 | Python | |||||
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy | ||||||||||
3d Mri Brain Tumor Segmentation Using Autoencoder Regularization | 243 | 3 years ago | 28 | mit | Jupyter Notebook | |||||
Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654). | ||||||||||
Awesome Tensorlayer | 212 | 4 years ago | 1 | cc0-1.0 | ||||||
A curated list of dedicated resources and applications | ||||||||||
Mvtec Anomaly Detection | 162 | 2 years ago | 21 | Python | ||||||
This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning. | ||||||||||
Unsupervised_anomaly_detection_brain_mri | 123 | 2 years ago | 1 | gpl-3.0 | Python | |||||
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study | ||||||||||
Segmentation | 94 | 5 years ago | 2 | Python | ||||||
Tensorflow implementation : U-net and FCN with global convolution | ||||||||||
Earthmapper | 59 | 6 years ago | 2 | Python | ||||||
Pipeline for the Semantic Segmentation (i.e., classification) of Remote Sensing Imagery | ||||||||||
Avgn | 52 | 3 years ago | 7 | mit | Jupyter Notebook | |||||
A generative network for animal vocalizations. For dimensionality reduction, sequencing, clustering, corpus-building, and generating novel 'stimulus spaces'. All with notebook examples using freely available datasets. | ||||||||||
Skin Segmentation Tensorflow | 35 | 6 years ago | mit | Python | ||||||
A modified SegNet Convolutional Neural Net for segmenting human skin from images | ||||||||||
Tf 1d 2d Segmentation End2endpipelines | 19 | a year ago | mit | Jupyter Notebook | ||||||
1D and 2D Segmentation Models with options such as Deep Supervision, Guided Attention, BiConvLSTM, Autoencoder, etc. |