U-Net is a deep learning architecture used for image segmentation tasks, particularly in medical imaging. It was proposed by Ronneberger et al. in 2015. The U-Net architecture consists of a contracting path and an expanding path. The contracting path is similar to a traditional convolutional neural network (CNN) architecture, where the input image is progressively downsampled to extract high-level features. The expanding path, on the other hand, is designed to recover the spatial resolution of the output segmentation mask by performing a series of upsampling operations.

The article talks about UNet architecture that is a deep learning model specifically designed for image segmentation tasks.

Deep Convolutional Neural Networks (CNNs) for image classification successively alternate convolutions and downsampling operations, such as pooling layers or strided convolutions, resulting in lower resolution features the deeper the network gets. These downsampling operations save computational resources and provide some translational invariance as well as a bigger receptive field at the next layers. However, an inherent side-effect of this is that high-level features, produced at the deep end of the network, are always captured in low resolution feature maps. The inverse is also true, as shallow layers always contain small scale features. In biomedical image analysis engineers are often tasked with classifying very small image patches which carry only a limited amount of information. By their nature, these patches may not even contain objects, with the classification depending instead on the detection of subtle underlying patterns with an unknown scale in the image's texture. In these cases every bit of information is valuable; thus, it is important to extract the maximum number of informative features possible. Driven by these considerations, we introduce a new CNN architecture which preserves multi-scale features from deep, intermediate, and shallow layers by utilizing skip connections along with consecutive contractions and expansions of the feature maps. Using a dataset of very low resolution patches from Pancreatic Ductal Adenocarcinoma (PDAC) CT scans we demonstrate that our network can outperform current state of the art models.

You can easily try out different backbones by selecting them from the Component's Backbone setting. And other settings like Activation, Output Activation, Pooling, and Unpooling methods, can just as easily be experimented with in a similar manner. From there, it's just a matter of viewing the training and validation results in Perceptilabs' Statistics View as you experiment with different values as shown in Figure 8: The Statistics View shows real-time metrics including the predicted segmentation overlayed on ground truth (upper left) and the Intersection Over Union (IoU) (middle right) for validation and training across epochs. IoU is a great method to assess the model's accuracy. It goes beyond pixel accuracy (which can be unbalanced due to having more background than object-level pixels) by comparing how much the objects in the output overlap those in ground truth. You can also view this for the model's test data in PerceptiLabs' Test View as shown in Figure 9: Alternatively, you can build U-Nets from scratch in PerceptiLabs.

This tutorial focus on the implementation of the image segmentation architecture called UNET in the PyTorch framework. It's a simple encoder-decoder architecture developed by Olaf Ronneberger et al. for Biomedical Image Segmentation in 2015 at the University of Freiburg, Germany. An image consists of multiple objects inside it, such as people, cars, animals, or any other object. To classify the image, we use image classification, where the task is to predict the label or class of the input image. Now imagine, we need to find the exact location of the object, i.e, which pixel belongs to the which object.

Today's blog post is going to be short and sweet. Today, we will be looking at how to implement the U-Net architecture in PyTorch in 60 lines of code. This blog is not an introduction to Image Segmentation or theoretical explanation of the U-Net architecture, for that, I would like to refer the reader to this wonderful article by Harshall Lamba. Rather, this blog post is a step-by-step explaination of how to implement U-Net from scratch in PyTorch. In this blogpost - first, we will understand the U-Net architecture - specifically, the input and output shapes of each block.

U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg, Germany.[1] The network is based on the fully convolutional network[2] and its architecture was modified and extended to work with fewer training images and to yield more precise segmentations. Segmentation of a 512 512 image takes less than a second on a modern GPU. The U-Net architecture stems from the so-called "fully convolutional network" first proposed by Long and Shelhamer.[2] The main idea is to supplement a usual contracting network by successive layers, where pooling operations are replaced by upsampling operators.

For several skin conditions such as vitiligo, accurate segmentation of lesions from skin images is the primary measure of disease progression and severity. Existing methods for vitiligo lesion segmentation require manual intervention. Unfortunately, manual segmentation is time and labor-intensive, as well as irreproducible between physicians. We introduce a convolutional neural network (CNN) that quickly and robustly performs vitiligo skin lesion segmentation. Our CNN has a U-Net architecture with a modified contracting path. We use the CNN to generate an initial segmentation of the lesion, then refine it by running the watershed algorithm on high-confidence pixels. We train the network on 247 images with a variety of lesion sizes, complexity, and anatomical sites. The network with our modifications noticeably outperforms the state-of-the-art U-Net, with a Jaccard Index (JI) score of 73.6% (compared to 36.7%). Moreover, our method requires only a few seconds for segmentation, in contrast with the previously proposed semi-autonomous watershed approach, which requires 2-29 minutes per image.

Image Classification helps us to classify what is contained in an image. The goal is to answer "is there a cat in this image?", Object Detection specifies the location of objects in the image. The goal is to identify "where is the cat in this image?", Image Segmentation creates a pixel-wise mask of each object in the images.

I participated in an amazing AI challenge through Omdena's community where we built a classification model for trees to prevent fires and save lives using satellite imagery. Omdena brings together AI enthusiasts from around the world to address real-world challenges through AI models. My primary responsibility was to manage the labeling task team. Afterward, I had the chance to take on another responsibility and build an AI model that delivered results beyond expectations. I am Leo from Rio de Janeiro, Brazil and I m a mechanical aeronautics engineer who currently works as a data scientist and management consultant in Brazil helping several companies to achieve better business results.