Advantages-of-Using-Machine-Learning-In Digital Pathology

What Are the Advantages of Using Machine Learning in Microscopy Image Analysis?

Machine learning algorithms integrated with whole slide imaging have become essential in modern digital pathology workflows. Furthermore, the shift to digitization has greatly impacted tissue and cell analysis within this field. Uncover how ML plays a crucial role in disease diagnosis, such as lymphoma cancer, and disease grading processes.


Machine learning (ML) algorithms integrated with whole slide imaging have firmly established their presence and forged a path toward becoming an indispensable component of this evolving workflow. The advent of digitization has exerted a profound influence on digital pathology, particularly in the realm of tissue and cell analysis. ML algorithms such as convolutional neural networks, recursive neural networks, Boltzmann machines, and others have brought numerous advantages like easier and faster sharing of images, better and more efficient consultation with peers, improved storage, constant quality, and numerous other additional features. ML includes image recognition, visual art and natural image processing, and bioinformatics. It is largely applied in electronic and medical record devices. Machine learning algorithms play a crucial role in facilitating the diagnosis of diseases, such as lymphoma cancer, while also extensively contributing to disease grading processes. Machine learning is an intriguing topic for diverse medical experts and the following text will explore its main features and advantages for your digital laboratory. 

The Growing Trend of Digitization of Pathology

There is practically no aspect of life that hasn’t been affected by digitization. This development in pathology was set in motion in the year 2000 when there was a commercialization of whole slide imaging that enabled getting the images at relatively high resolution. There are numerous drivers for the growing use of computer screens instead of traditional microscopes that have been used. Easier and faster sharing of histologic images, better and faster consultation with colleagues, improved storage of stained slides with the maintenance of constant quality, and quick annotation of relevant features in the whole-slide images are just a few of them. In combination with the development of software for image analysis, scanned histology slides further pave the way for the development of tools that extract highly quantitative data from the digitized tissue sections. This convergence of technologies has greatly enhanced the value of digital pathology and image sections as indispensable assets for translational tissue-based research.  

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Advantages of Machine Learning in Digital Pathology

There are three major advantages of ML in digital pathology. It improves accuracy and speed, enables discoveries, and completes tasks with consistency. Machine learning is a prominent field in artificial intelligence (AI), and one of its notable subsets is deep learning. Deep learning relies on artificial neural networks, intricate systems of interconnected algorithms designed to receive and analyze information in a layered fashion. 

Improved Accuracy and Speed

Deep learning represents a groundbreaking advancement as it marks the first instance in history where machines can effectively emulate human abilities to discern intricate visual patterns. Its remarkable speed, heightened accuracy, and ability to surpass human capabilities set deep learning apart. Through deep learning, the analysis of biological samples can yield precise and quantitative information. Pathologists can automate manual and time-consuming image analysis work and focus on more important tasks. 

New Discoveries Enabled

Deep learning possesses the ability to perceive elements that may elude the human eye. In instances where features are exceedingly small, exhibit heterogeneity in expression, or are dispersed across a vast area, machine learning excels in their recognition. This proficiency unlocks the potential to enhance the process of discovery, enabling AI to detect even the most minute alterations. Moreover, by leveraging ML, one can train AI models to extract and visualize features associated with the outcomes of their work, facilitating deeper insights. 

Tasks are Completed Consistently

In numerous research domains, diverse sample types, and diagnostic settings, the presence of inter and intra-observer subjectivity poses a significant concern in image analysis. Maintaining objectivity across these areas remains a pressing challenge. For the best whole slide imaging services, ScanPoint from PreciPoint allows you to work with high-quality digital images most conveniently. ScanPoint scans standard as well as unconventional dimension slides and empowers you to interpret digital slides and do away with tons of physical samples. 

Maintains Performance

Deep learning consistently maintains its performance, offering stability in its outcomes. AI algorithms reliably align with the provided ground truth, effortlessly classifying results and effectively addressing the challenges you encounter. 

Benefits of Self-trained Algorithms

If you applied deep learning to your medical practice, you could create your own deep learning AI models and automate your work. PreciAI removes the complexities often associated with image analysis. Its intuitive point-and-click interface makes training effortless, enabling users to navigate through the digital images effortlessly. You don’t need to know how to code or have specific hardware. PreciAI deep learning AI is your assistant that can help you to improve yourself and discover more in your work. 


In the rapidly advancing field of microscopy image analysis, machine learning (ML) emerges as a game-changer, revolutionizing the way researchers extract valuable insights from their data. Through the power of machine learning algorithms, the realm of microscopy analysis has witnessed unprecedented advancements, enabling automated and accurate detection, classification, and quantification of complex features. By leveraging the vast potential of machine learning, researchers and pathologists can now unlock new dimensions of knowledge. Some of the advantages of ML in digital pathology include improved accuracy and speed, enabling discoveries, and the use of self-trained algorithms to maintain performance. With whole slide imaging services such as PreciPoint’s ScanPoint and PreciAI, you can fluently work with high-quality digital images most conveniently. While ScanPoint scans your physical slides to digital ones, PreciAI-trained ML algorithms simplify the process, enabling you to focus on your scientific objectives rather than technical intricacies.