keras-unet-collection 0.0.3

The tensorflow.keras implementations of U-net, U-net++, Residual U-net, Attention U-net.

keras-unet-collection

This repository contains tensorflow.keras implementations of U-net, U-net++, Residual U-net, Attention U-net. Details of these models are listed as follows:

• [U-net/Unet] Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

• [U-net++/Unet++] Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N. and Liang, J., 2018. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3-11). Springer, Cham.

• [R2U-Net] Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M. and Asari, V.K., 2018. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955.

• [Attention U-net] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B. and Glocker, B., 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.

Dependencies

• TensorFlow 2.3.0

• Keras 2.4.0

Installation and usage

pip install keras-unet-collection

from keras_unet_collection import models
# e.g. models.unet_2d(...)

• Jupyter notebooks are provided as user guides.

Versions	Release date	Updates
0.0.2	2020-12-30	(1) CRPS loss function. (2) Semi-hard triplet loss function. (3) Fixing user specified names on keras models.
0.0.3	2020-01-01	(1) Bug fix. (2) keyword and documentation fixes for R2U-Net.

Overview

U-net is a convolutional neural network with encoder-decoder architecture and skip-connections, loosely defined under the concept of "fully convolutional networks." U-net was originally proposed for the semantic segmentation of medical images and is modified for solving a wider range of gridded learning problems.

U-net and many of its variants take three or four-dimensional tensors as inputs and produce outputs of the same shape. One technical highlight of these models is the skip-connections from downsampling to upsampling layers that benefit the reconstruction of high-resolution, gridded outputs.

Contact

Yingkai (Kyle) Sha [e-mail]

License

MIT License