TY  - CONF
AU  - Reza Azad
AU  - Maryam Asadi Aghbolaghi
AU  - Mahmood Fathy
AU  - Sergio Escalera
A2  - ICCVW
PY  - 2019//
TI  - Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
BT  - Visual Recognition for Medical Images workshop
SP  - 406
EP  - 415
N2  - 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.
UR  - https://ieeexplore.ieee.org/document/9022282
L1  - http://refbase.cvc.uab.es/files/AAF2019.pdf
UR  - http://dx.doi.org/10.1109/ICCVW.2019.00052
N1  - HUPBA; no proj
ID  - Reza Azad2019
ER  -