BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

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Deep Learning BT-Unet
Overview

BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

Deep learning has brought most profound contribution towards biomedical image segmentation to automate the process of delineation in the medical imaging. To accomplish such task, the models are required to be trained using huge amount of annotated or labelled data that highlights the region of interest with binary mask. However, efficient generation of the annotations for such huge data require expert biomedical analyst and extensive manual effort. It is a tedious and expensive task, while also being vulnerable to human error. To address this problem, a self-supervised learning framework, BT-Unet is proposed that uses Barlow Twins approach to pre-train the encoder of a U-Net model via redundancy reduction using unlabeled data to learn data representation, later complete network is fine-tuned to perform actual segmentation. BT-Unet framework can be trained with limited number of annotated samples while having high number of unannotated samples, which is mostly the case in real-world problems.

This repository includes the implementation of BT-Unet framework which can easily be integrated with any U-Net model.

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Comments
  • AVG pooling

    AVG pooling

    Hello,

    I'm also trying to use BT for segmentation

    I've noticed in your paper code, that the global average pooling restrict is a huge bottleneck: image

    According to my experiments, in your BT-Unet: before pooling: batch x 16x16x256 After pooling: batchx256,

    Then we are left with only 256 value per image to be fed to the FC layers, which is very few I think.

    In the original BT paper, they also do an AVG pooling (well, actually it's built in into the Resnet they use), before pooling: batch x2048x7x7 after pooling: batch x2048x1x1

    As you can see, original paper has much more data per image (2048 instead of 256)

    What do you think ? Am I correct ?

    opened by joihn 1
  • efficiency proposal

    efficiency proposal

    https://github.com/nspunn1993/BT-Unet/blob/main/BT-Unet.py#L296 z_a, z_b = self.encoder(ds_one, training=True), self.encoder(ds_two, training=True)

    I guess here we could concatenate the input along the batch axis, do a single pass in the net, then split the output. This should lead to a more efficient and faster training,

    opened by joihn 1
Owner
Narinder Singh Punn
Research scholar
Narinder Singh Punn
GitHub
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