In this article, I will try to summarise Residual UNet, its architecture, and applications. Previously, I had covered Basic UNet, 3D UNet, and Attention UNet, all of which can be found here.

Image from Fusion Alliance

In the case of medical image segmentation e.g. lung segmentation from CT images, a lot of challenges are…

Hey, y’all! I started writing about network architectures useful for medical image segmentation i.e. UNet and its variants. In the first article, I had covered basic UNet and 3D UNet. You can find that here. In this article, I'm going to go over Attention UNet.

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Attention UNet

Fully convolutional neural networks (FCNNs)…

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Medical image segmentation is an important area in medical image analysis and is necessary for diagnosis, monitoring, and treatment. The deep learning-based methods have achieved superior performance compared to traditional methods in medical image segmentation tasks. …

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Domain adaptation is an important topic in recent studies on deep learning, that aims to recover performance degradation when applying the neural networks to new testing domains. SIFA also called Synergistic Image and Feature Adaptation is a novel unsupervised domain adaptation framework that effectively tackles the problem of domain shift.


I have been working on a Covid CT dataset from Kaggle containing 20 CT scans of patients diagnosed with COVID-19 as well as segmentation of lungs and infections made by experts. My goal was to build a segmentation model using UNet. Due to fewer images (only 20 ;P), I had…

Shambhavi Malik

Biomed and DL enthusiast (IIT BHU ‘23)

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