Artificial intelligence (AI) has made remarkable progress in recent years, particularly in natural language processing (NLP). One of the most significant developments has been the rise of large language models (LLMs)—powerful models that can easily understand and generate human language. These models have revolutionized various industries, enabling everything from automatic Continue Reading
Deep Learning
What is Image Captioning?
In recent years, the field of artificial intelligence (AI) has seen remarkable advancements, particularly in how machines can understand and describe visual content. One of the fascinating developments in this area is image captioning, where AI models are trained to generate descriptive captions for images. This technology, often referred to Continue Reading
What is Dice Coefficient?
This article will explore the Dice Coefficient (DSC), a metric commonly used to evaluate the similarity between two sets. We’ll delve into its definition, provide implementations in NumPy, TensorFlow, and PyTorch, and discuss its practical applications. By the end of this guide, you’ll have a solid understanding of the Dice Continue Reading
Generative and Discriminative Models in Machine Learning
Machine learning is a fascinating field that teaches computers to make decisions or predictions based on data. Two main types of models are commonly used: generative models and discriminative models. These models have different approaches to learning from data, and understanding them can help you choose the right one for 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