We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

lib:0b84a85a58fea420 (v1.0.0)

Vote to reproduce this paper and share portable workflows   1 
Authors: Reza Azad,Maryam Asadi-Aghbolaghi,Mahmood Fathy,Sergio Escalera
ArXiv: 1909.00166
Document:  PDF  DOI 
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/1909.00166v1

In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!