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Junjun2016 5dacca3ea8
Add 4 retinal vessel segmentation benchmark (#315) 
* add 4 retinal vessel segmentation configs of UNet

* fix flip augmentation

* add unet benchmark on 4 medical datasets

* fix hrf bug
2020-12-23 23:58:09 -08:00
..
README.md
Add 4 retinal vessel segmentation benchmark (#315)
2020-12-23 23:58:09 -08:00
unet_s5-d16_64x64_40k_drive.py
Add 4 retinal vessel segmentation benchmark (#315)
2020-12-23 23:58:09 -08:00
unet_s5-d16_128x128_40k_chase_db1.py
Add 4 retinal vessel segmentation benchmark (#315)
2020-12-23 23:58:09 -08:00
unet_s5-d16_128x128_40k_stare.py
Add 4 retinal vessel segmentation benchmark (#315)
2020-12-23 23:58:09 -08:00
unet_s5-d16_256x256_40k_hrf.py
Add 4 retinal vessel segmentation benchmark (#315)
2020-12-23 23:58:09 -08:00

README.md

U-Net: Convolutional Networks for Biomedical Image Segmentation

Introduction

@inproceedings{ronneberger2015u,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={International Conference on Medical image computing and computer-assisted intervention},
  pages={234--241},
  year={2015},
  organization={Springer}
}

Results and models

Backbone Head Dataset Image Size Crop Size Stride Lr schd Mem (GB) Inf time (fps) Dice download
UNet-S5-D16 FCN DRIVE 584x565 64x64 42x42 40000 0.680 - 78.67 model | log
UNet-S5-D16 FCN STARE 605x700 128x128 85x85 40000 0.968 - 81.02 model | log
UNet-S5-D16 FCN CHASE_DB1 960x999 128x128 85x85 40000 0.968 - 80.24 model | log
UNet-S5-D16 FCN HRF 2336x3504 256x256 170x170 40000 2.525 - 79.45 model | log