Brief Review — BT‑Unet: A self‑supervised learning framework for biomedical image segmentation using barlow twins with U‑net models
BT‑Unet, Pretrain U-Net with Barlow Twins
BT‑Unet: A self‑supervised learning framework for biomedical image segmentation using barlow twins with U‑net models,
BT-Unet, by Indian Institute of Information Technology Allahabad,
2022 JML (Sik-Ho Tsang @ Medium)
- BT-Unet is proposed that uses the Barlow Twins approach to pre-train the encoder of a U-Net model via redundancy reduction in an unsupervised manner to learn data representation.
- Later, complete network is fine-tuned to perform actual segmentation.
- The BT-Unet framework is divided into two phases: 1) Pre-training, and 2) Fine-tuning.
- In pre-training, the aim is to learn the complex feature representations using unannotated data samples.
- The encoder of the U-Net models is pre-trained with the Barlow Twins (BT) strategy and later fine-tuned to perform actual segmentation:
- (Please feel free to read Barlow Twins if interested.)
- BT-Unet framework is applied to various state-of-the-art U-Net models: vanilla U-Net, Attention U-Net (A-Unet), inception U-Net (I-Unet) and residual cross-spatial attention guided inception U-Net (RCA-IUnet).
- The weights of the encoder network in the U-Net model are initialized with pre-trained weights (from the first phase), whereas the rest of the network is initialized with default weights.
- Finally, the U-Net model is fine-tuned with limited annotated samples for the biomedical image segmentation.
- U-Net models are fine-tuned with segmentation loss function, L defined as the average of binary cross-entropy loss, LBC and dice coefficient loss, LDC:
- where y is the ground truth label of a pixel, p(y) is the predicted label of a pixel and N is the total number of pixels.
2.2. Performance Using Fixed Small Training Set
- KDSB18: The performance of the BT enabled U-Net models exceeds as compared to the models without BT.
- BUSIS: U-Net and A-Unet models are not able to learn and extract feature maps concerning tumor regions (achieved 0 precision, DC and mIoU), however with pre-training, these models achieved noticeable improvement. In case of I-Unet and RCAIUnet models, considerable improvements are observed with pre-training.
- ISIC18: The I-Unet and RCAIUnet models are the most influenced networks that achieved 5.1% and 2.2% increase in precision respectively. However, a slight decline in performance is observed with vanilla U-Net and A-Unet while using BT pre-training.
- BraTS18: I-Unet and RCA-IUnet models achieved significant gain in the segmentation performance while using the BT-Unet framework, whereas the same behavior is not observed with vanilla U-Net and A-Unet models.
2.3. Performance Using Different-Sized Small Training Set
- For all datasets with training fractions less than 50%, similar change in performance is observed among the models.
2.4. Qualitative Results
- RCA-IUNet with BT has a very good segmentation result.
Hope I can review Inception U-Net and RCA-IUNet in the coming future.
[2022 JML] [BT-Unet]
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models
4.4. Biomedical Image Self-Supervised Learning
2018 [Spitzer MICCAI’18] 2019 [Rubik’s Cube] [Context Restoration] 2020 [ConVIRT] [Rubik’s Cube+] 2021 [MICLe] [MoCo-CXR] 2022 [BT-Unet]