rezazad68/BCDU-Net
A bi-directional ConvLSTM U-Net with densely connected convolutions for medical image segmentation.
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This repository implements BCDU-Net, a deep autoencoder-decoder architecture for semantic segmentation of medical images. It combines bidirectional convolutional LSTM layers with U-Net structure to capture both semantic and high-resolution information. The model also uses densely connected convolutions and batch normalization for improved feature representation and convergence. It achieves state-of-the-art results on skin lesion, lung, and retinal blood vessel segmentation tasks.
Frequently asked
- What is rezazad68/BCDU-Net?
- A bi-directional ConvLSTM U-Net with densely connected convolutions for medical image segmentation.
- Is BCDU-Net open source?
- Yes — rezazad68/BCDU-Net is an open-source project tracked on heatdrop.
- What language is BCDU-Net written in?
- rezazad68/BCDU-Net is primarily written in Python.
- How popular is BCDU-Net?
- rezazad68/BCDU-Net has 786 stars on GitHub.
- Where can I find BCDU-Net?
- rezazad68/BCDU-Net is on GitHub at https://github.com/rezazad68/BCDU-Net.