zakidotai / autoencoders-using-pytorch-medical-imaging

Medical Imaging, Denoising Autoencoder, Sparse Denoising Autoencoder (SDAE) End-to-end and Layer Wise Pretraining

Autoencoders-using-Pytorch

In this project, nuances of the autoencoder training were looked over.

1. Autoencoder end-to-end training for classifying MNIST dataset. [Notebook01]
2. Autoencoder Layer Wise Pre-training (Stacking) for Fashion-MNIST. [Notebook02]
3. DRIVE (Digital Retinal Images for Vessel Extractions) dataset patchwise segmentation using Autoencoder. [Notebook03]
4. Sparse Denoising Autoencoder (SDAE) for classification of MNIST dataset. [Notebook04, Notebook05]

Built With

• Pytorch framework

License

This project is licensed under the MIT License - see the LICENSE.md file for details