Post by romanasimran2024 on Feb 11, 2024 1:14:52 GMT -8
Histopathology constitutes a crucial source of information for clinicians and researchers. These are images obtained from human tissue samples , which play a fundamental role in the diagnosis and treatment of various diseases, including cancer.
Traditionally, histopathological analysis has been Argentina Telemarketing Data performed by human pathologists, who examine the images generated by the microscope one by one and issue a diagnosis based on their experience and knowledge . However, this process is laborious, subjective and subject to possible human error, since it depends largely on the expert.
training health dataIn this context, artificial intelligence (AI) can be very helpful in transforming this medical practice. In recent years, it has been shown that the application of machine learning techniques in the analysis of medical images allows obtaining very promising results, although it is true that, despite recent advances, the implementation of algorithms for the specific analysis of images histopathology is a field still developing.
For this reason, at the IIC we have investigated the performance of Deep Learning algorithms in the analysis of histopathological images. Based on the fact that AI is capable of identifying subtle patterns and characteristics that can sometimes escape the human eye, this can provide support and improvement in clinical decision making , a faster and more accurate diagnosis, as well as greater objectivity in the interpretation of the images.
Three tasks for image analysis At the IIC, we analyze and interpret all types of images generated in the health sector . To do this, the three main tasks of image analysis must be considered: classification, detection and segmentation. Each of them addresses different aspects of the analysis:
Image classification involves assigning a predefined label or category to an entire image. The goal is to determine which class or category the image as a whole belongs to.
Image detection focuses on identifying and locating objects or regions of interest within an image. Unlike classification, where the goal is to label the entire image, detection focuses on detecting the presence of individual objects and drawing a bounding box around them. This provides precise information about the location and classes associated with the detected objects.