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Machine learning for beginners

Machine learning is playing an increasingly essential role in the field of medical imaging. It includes problems as diverse as computer-assisted diagnosis, lesion detection, segmentation of anatomical regions, brain mapping, image-guided therapy, image annotation, and database retrieval. All these elements seem quite complicated to comprehend, but what you should understand in a nutshell is that today’s radiology is completely different from 10 years ago. 

For a wide spectrum of medical and scientific applications, clinical pictures are a rich and useful source of knowledge. Additionally, the technology used to collect medical pictures is continually improving, resulting in higher-quality scans of formerly non-observable biological structures, at affordable rates. Imaging scans will represent a large component of the rising archives of digital biomedical data in the next century of data-driven public healthcare. 

Intro into computer science? 

Machine learning (ML) is the science of algorithms that learn from data, develop models from that input and use those models to forecast, make decisions, or solve different problems. The learning activity and the logic module are the two main components of ML. The learning module creates a model using information such as background data and previous knowledge. The logic module employs frameworks that registers an answer to the task as well as a performance rating. It can anticipate or make judgments by generating a mathematical model learned from the training material. 

The enormous variety in anatomy between humans, as well as the intricacies of our own body makes the job of a radiologist extremely complicated. Machine learning, which has so far mostly concentrated on nonmedical AI applications like computer vision, has the chance to be a critical toolset in harnessing the potential of diagnostic imaging. Let’s dive deeper into the 4 main techniques used by artificial intelligence that improves this area. 

Supervised learning as a way of solving 

Supervised learning indicates the presence of a supervisor or teacher. This is a learning technique in which we teach or train the machine using data that is already labelled. This means that there should be an answer regarding each question. The machine is provided with a new set and the supervised learning algorithm analyses the training data and produces a correct result from labeled data. 

The Supervised learning computation ultimate objective is to predict an answer with the highest possible level of accuracy for a given new input.

Unsupervised Learning it’s the how we learn 

Unsupervised learning is the training of machine using information that is not labeled and allows the algorithm to produce results on that information, without guidance. Here, the function of the machine is to classify unsorted information according to similarities, patterns and differences without any prior training of data. There is no teacher algorithm like supervised learning algorithm. There is a consensus that the way humans learn is through unsupervised learning. As a child, we don’t need to be shown thousands of objects in order to recognize them. After only a few examples, we learn to differentiate in great detail. When we don’t label our data, we are doing unsupervised learning.

Semi Supervised Learning and the Great Unkown 

Semi-supervised learning is a class of machine learning techniques that also make use of unlabeled data for training. Here, a small amount of labeled data is mixed with a large amount of unlabeled data. Semi-supervised learning falls between unsupervised learning and supervised learning (with training data). Why is Semi-Supervised Machine Learning Important?

When we don’t have enough labeled data to produce an accurate model and we don’t have the ability or resources to get more data, we can use semi-supervised techniques to increase the size of our training data. Consider the case where we’re working on a model to detect cancer in a chest scan. While we are aware of the appearance of some forms of neoplasia, we are unaware of other cases of malignancies. We can tag the dataset with cancer cases we know about, but the rest of our data will be left unlabeled. 

Reinforcement Learning Algorithm 

Reinforcement learning algorithm is training from a particular situation and action. It finds the best possible behavior or path for various software and machines, considering a specific situation. This is a ML technique that allows an agent to train in an interactive environment through trial and error, while receiving feedback from its own events and experiences. 

Actual experience in the hospital 

Now that we understand how machine learning works, what exactly it can do in practice? According to Peter Eggleston, global marketing director for GE Healthcare, “AI is making radiology professionals more productive, boosting quantity, and enhancing the precision of their work.” There are three stages in which the intelligence of a computer can assist the clinician:  

  • Productivity: AI streamlines radiology procedures by automating and prioritizing routine and repetitive tasks. As AI develops a work queue, less time is wasted travelling between discordant activities. 
  • Quantity: AI technologies and apps can automatically or semi-automatically extract and quantify data without the need of a doctor to do it. 
  • Precision: AI enhances accuracy by ensuring that the correct information is available, that non-useful information is separated, and that quantitative processes are repeatable.

Not only are uprising technologies transforming the way physicians work, but they are also changing where they may work. With these emerging innovations, image analysis software that was previously only available on the local workstation that came with an imaging equipment may now be used on any desktop, locally or remotely.  

AI-based reconstructing techniques can be accessible through server or cloud-based image processing software applications. A single person can work with data from several sources or workstations at the same time, and the volume of concurrent users can be adjusted to fit the department’s needs. 

This is where our company makes progress and moves forward. 

XVision was founded on the notion that Artificial Intelligence can be the radiologist practitioner’s always-on assistant and the patient’s most trustworthy friend. Our team has a unique deep relationship to the complexities of the radiology profession, as well as the knowledge and education necessary to realize AI’s full and practical potential. 

XVision’s software is used internationally in over 60 clinics and hospitals, and analyzes over 26,000 chest X-rays and 10,000 lung CTs per month. 

You can also read about how XVision was used in a national screening program for tuberculosis by clicking here.

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