Track 3: Artificial Intelligence in Digital Pathology

July 29, 2022

Track 3: Artificial Intelligence in Digital Pathology

Pathology AI systems are computer programs that help pathologists with their work or provide automated pathology. A Pathology AI system's main capability is to analyze digital slide images using image analysis and machine learning.

Digital Pathology enables for the scanning of slides and the replacement of the microscope with a computer monitor. Digital Pathology only provides the convenience of dealing with images rather than glass slides, but by digitizing glass slides to images, image analysis and machine learning can be used for tissue analysis.

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Pathology AI (Artificial Intelligence) :

Pathology AI systems are computer programs to help pathologists with their work or provide automated pathology. A Pathology AI system's main capability is to analyze digital slide images using image analysis and machine learning. Machine learning can learn a task from data, such as providing a diagnosis or a score, or a subtask, such as classifying cells into different cell types. There are numerous approaches to machine learning, such as decision trees, random forests, and deep learning, on which we will concentrate our discussion. Deep learning has created a buzz around Artificial Intelligence in recent years (AI). Deep learning has overcome significant challenges in computer vision, where feature detection could not be successfully implemented by programming image analysis algorithms. A deep learning network can learn highly complex visual features from image data alone, outperforming expert people. Deep learning necessitates a large amount of data as well as a large amount of processing power. However, with increased processing power and, in particular, the use of GPUs, it is now possible to successfully train deep learning networks. AlexNet was the first deep learning network to make a significant breakthrough in 2012, outperforming all previous approaches on the ImageNet challenge, a large visual database designed for object recognition software research. Every year since then, more efficient and high-performance systems have been introduced. Because pathology is a visual task, it is understandable that deep learning is making its way into the field. In 2016 and 2017, there was a Grand Challenge in Biomedical Image Analysis, CAMELYON 16 and CAMELYON 17, on cancer metastasis detection in lymph nodes, which deep learning clearly dominated and won. Deep learning network design is difficult; it is no longer just about finding the right hyperparameters, but also about designing new network topologies; it is an art! As a result, many applications begin by reusing existing designs that have proven themselves in other applications, such as the CAMELYON 16 challenge winner, who re-used the GoogLeNet that won the ImageNet challenge in 2014. Because pathology applications differ from general-purpose image recognition tasks, hand-crafting an appropriate net topology for pathology applications, focusing on cell-data, could yield significant benefits. Academia has only recently begun to work on digital slide images, but most of them are now motivated by the hype surrounding deep learning. Deep learning has great value in situations where feature detection presents a challenge for traditional image analysis, but it comes at a cost

(a) learning data sets are expensive, (b) there is a risk of bias from the training data, and

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Reference Digital Pathology UCGconferences press releases and blogs

https://medium.com/@taania.ucg/what-is-digital-pathology-932897b40e03

https://kikoxp.com/posts/13185

https://qr.ae/pvPmWL

https://sites.google.com/view/digitalpathologyucg/what-is-digital-pathology

https://digitalpathologyucg.blogspot.com/2022/07/what-is-digital-pathology.html

https://digitalpathologyucg915618148.wordpress.com/2022/07/04/what-is-digital-pathology/