![[Paper Summary] EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://idiotdeveloper.com/wp-content/uploads/2024/09/Paper-Summary-EMCAD-Efficient-Multi-scale-Convolutional-Attention-Decoding-for-Medical-Image-Segmentation-1024x536.jpg)
This post will analyze the research paper “EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation.” We will discuss the problems with existing medical image segmentation methods and how the given method (EMCAD) solves these issues. What is EMCAD? EMCAD is a newly developed efficient multi-scale convolutional attention decoder Continue Reading
A Brief History of Large Large Language Models (LLMs)
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
What is a Large Language Model (LLM)?
Large Language Models (LLMs) have revolutionized the field of artificial intelligence (AI) by enabling machines to understand and generate human language in a once unimaginable way. These powerful models, built using vast datasets and sophisticated algorithms, are now at the core of numerous applications, from chatbots and virtual assistants to Continue Reading
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
Read Video Files Using OpenCV Python
Reading and processing video files is a common task in computer vision, and OpenCV makes it easy to work with video data. In this article, we’ll go through the process of reading and displaying video files using OpenCV. Whether you’re working on a video analysis project or want to learn 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
Image Masking with OpenCV AddWeighted
Image masking is a powerful technique used in image processing to manipulate specific parts of an image while leaving other areas untouched. This is particularly useful in applications like object detection, image segmentation, and photo editing. In this tutorial, we’ll explore how to perform image masking using OpenCV addWeighted function. 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