je-santos / resunet-with-crf-and-tta

Improving Semantic Image Segmentation and Generalization with ResUNet++, Conditional Random Field, and Test-Time Augmentation

ResUNet++-with-Conditional-Random-Field-and-Test-Time-Augmentation

ResUNet++

The ResUNet++ architecture is based on the Deep Residual U-Net (ResUNet), which is an architecture that uses the strength of deep residual learning and U-Net. The proposed ResUNet++ architecture takes advantage of the residual blocks, the squeeze and excitation block, ASPP, and the attention block.

ResUNet++: An Advanced Architecture for Medical Image Segmentation

Architecture

Datasets:

The following datasets are used in this experiment:

1. Kvasir-SEG
2. CVC-ClinicDB
3. CVC-ColonDB
4. ETIS-Larib polyp DB

Hyperparameters:

1. Batch size = 16
2. Number of epoch = 300
3. Loss = Binary crossentropy
4. Optimizer = Nadam
5. Learning Rate = 1e-5 (Adjusted for some experiments)

Results

Qualitative result comparison of the proposed models with UNet, ResUNet, and ResUNet++.