In high-stakes fields like medical imaging, autonomous driving, and remote sensing, a wrong prediction made with high confidence can be catastrophic. That’s where Uncertainty Estimation steps in—empowering your model to express doubt. And with techniques like Monte Carlo Dropout, you can transform any deterministic deep network into a model that Continue Reading
UNet
Test Time Augmentation (TTA) for Segmentation in PyTorch
In recent posts, we’ve built a strong foundation around multiclass image segmentation using PyTorch. From creating segmentation masks, converting RGB to class index masks, overlaying results using OpenCV, to training a full-fledged UNet model and visualizing it with GradCAM, we’ve covered the full training pipeline. But what happens when your Continue Reading
GradCAM Heatmaps for Segmentation with UNet in PyTorch
In semantic segmentation, understanding how a deep learning model arrives at its decisions is crucial—especially in fields like medical imaging, agriculture, and autonomous systems. While U-Net and other architectures can deliver high accuracy, they often act as black boxes. In this blog post, we go beyond prediction accuracy. We’ll visualize Continue Reading
Multiclass Segmentation in PyTorch using U-Net
Semantic segmentation is a crucial task in computer vision that involves labeling each pixel in an image with its corresponding class. In this blog post, we’ll dive into building a multiclass semantic segmentation pipeline using the U-Net architecture with PyTorch. Our goal is to segment different types of weeds from Continue Reading
ResUNet++ Implementation in TensorFlow
In this article, we will study the ResUNet++ architecture and implement it using the TensorFlow framework. ResUNet++ is a medical image segmentation architecture built upon the ResUNet architecture. It takes advantage of Residual Networks, Squeeze and Excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP), and attention blocks. What is ResUNet++? Debesh Continue Reading
UNet 3+ Implementation in TensorFlow
In this article, we will implement the UNet 3+ architecture using TensorFlow. UNet 3+ extends the classic UNet and UNet++ architecture incorporating full skip connections. We will delve into each block of the UNet 3+ architecture, explaining how they work and how they contribute to improving the model’s performance. Understanding these Continue Reading
Skip Connection in Image Segmentation: UNet, UNet++ and UNet 3+
Image segmentation, a fundamental task in computer vision, involves partitioning an image into multiple segments to simplify its representation. One of the critical advancements in image segmentation architectures is the integration of skip connections, which have revolutionized the field by improving the accuracy and efficiency of segmentation models. What are Continue Reading
[Paper Summary] UNet 3+: A Full-Scale Connected UNET For Medical Image Segmentation
![[Paper Summary] UNet 3+: A Full-Scale Connected UNET For Medical Image Segmentation](https://idiotdeveloper.com/wp-content/uploads/2024/02/ResU-Net-1.jpg)
In medical image analysis, accurately identifying and outlining organs is vital for clinical applications such as diagnosis and treatment planning. The UNet architecture, a widely favoured choice for these tasks, has seen enhancements through UNet++, which introduced nested and dense skip connections to improve performance. Taking this evolution further, the Continue Reading
ResUNET: A TensorFlow Implementation for Semantic Segmentation
In computer vision and medical image analysis, semantic segmentation plays a pivotal role in understanding and interpreting visual data. One of the prominent architectures in this domain is ResUNet, a fusion of U-Net and ResNet architectures, renowned for its ability to efficiently capture local and global features. In this blog Continue Reading
ColonSegNet Implementation In TensorFlow
In this article, we will explore the technical intricacies of implementing ColonSegNet in TensorFlow. ColonSegNet is a lightweight, real-time colon segmentation architecture that has garnered attention for its efficiency in medical image analysis. In our previous post, we introduced the architecture and its components. Now, let’s explore the technical intricacies Continue Reading