Post by simranratry20244 on Feb 12, 2024 4:34:13 GMT -5
One of the main advantages of synthetic image generation is that it allows us to overcome data scarcity since we are not dependent on real data sets. Instead, we can generate a synthetic data set that resembles real data and train models and perform tests without requiring large amounts of images. Throughout 2022, there was a boom in diffusion models to generate images from texts . Thanks to the use of these models, it is possible to obtain synthetic images that are sufficiently similar to those of the problem to be solved and, in this way, be able to solve image classification tasks without data.
These diffusion models for generating images are in full swing and Colombia Telemarketing Data continuous development. The images that can be generated with diffusion models are increasingly complex and precise. By choosing the correct prompt – or text – you can create images with a high degree of specificity and with a high similarity to the data of a specific problem.
Case of classification of images generated with Stable Diffusion. To exemplify the application of diffusion models in image generation, a well-known data set in the field of images will be used: “Dog vs Cats” . This is a binary image classification problem in which the model has to predict whether an image contains a dog or a cat. In this case, only the test images are needed, since images generated synthetically through the Stable Diffusion 1 model will be used to train the model.
Using Stable Diffusion , 3000 synthetic images of dogs and cats have been automatically generated (1500 of each category). These images represent the training set that will be used to train the CNN model. In this case, a Resnet50 has been used as the architecture. After training the model with the synthetic images , the results obtained in the test set are almost perfect, obtaining an F1 above 0.99. These results demonstrate, in this problem, that a model trained with synthetic images can successfully infer on real data.
The event was once again an opportunity to listen, learn and discuss clinical experiences, the current situation and the future of the Sepsis Code in Spain , but also in other reference countries. In addition, those involved were able to reflect on the evolution of the program, which has already proven to improve patient care. Sepsis Meeting Madrid Part of that success lies in the multidisciplinary philosophy of the Sepsis Code, which it shares with the IIC. We are committed to working between different professionals and institutions to improve a clinical process that in the end is a health emergency.
Early detection of sepsis Furthermore, at this meeting, technology was once again a central topic, as a tool to more effectively address the diagnosis and treatment of this time-dependent disease. Specifically, they talked again about BISEPRO , the Artificial Intelligence system developed by the IIC for the early detection of sepsis. This early warning system continues to be of interest to the scientific community and also has its space this year at the XXVI National Congress of Health Informatics (Inforsalud 2023), where the process of implementation in the electronic medical record of patients is focused on hospitals.