Machine Learning in Medical Imaging and Analysis
Posted By : Anirudh Bhardwaj | 24-Dec-2018
As per IBM, over 90% of the medical data rely on images in one way or another. Owing to the tremendous advancements in image recognition techniques, medical sciences are making significant progress, especially in the field transfer learning with pre-tuned networks on an imageNet dataset.
As it turns out, automated image diagnosis is making headway in medical sciences these days and is expected to bring up to $3 billion worth of revenue in the coming years. With AI under its belt, the healthcare sector is now able to tackle complex image-related challenges to address many problems persisting in the industry.
The Age of Radiogenomics
It has been proved that Imaging techniques, when combined with genetic data, can bring significant benefits in diagnostics and therapy. Collectively, they have given birth to a new discipline of radiogenomics that connects images with expressions of gene patterns in order to map modalities.
Apparently, the genetic data can also be analyzed through Natural Language Processing (NLP) techniques. However, Image recognition methods are best suited when the genomic data represents a one-dimensional picture, with each color denoting a different gene. Also, it incorporates the same algorithms as any other image recognition approach. On contrary, NLP is most commonly used to find patterns present in larger sequences of genes.
Challenges With Image Analysis
When it comes to Image Analysis, an image is no longer considered just a visual in data sciences. Rather, it represents much more and accounts for the encoded digital information that can reveal so much more about the visual elements in it than one could possibly imagine. Each pixel carries a certain value and holds important biophysical properties. As it turns out, it is now possible for the programmers to find patterns across the image representing a certain disease. Alas, the procedure is quite time-consuming and doesn’t attain the optimum level of accuracy in representing a disease.
Machine Learning Might Be The Solution
Machine Learning, a subfield of AI which gives computers the ability to learn from data without being explicitly programmed might be the helping in fixing these drawbacks. Researchers claim that if a Machine Learning algorithm is fed with sufficient data, it can not only find and reveal relevant patterns in images but also speed up the process with sheer accuracy. Nevertheless, a Machine Learning algorithm will always keep learning and improving after each session, so it will know where to look for the relevant patterns. Therefore, the speed and accuracy of the system will only increase with time. However, note that the quality and efficiency of a machine learning algorithm very much depends on the amount of data you feed it.
Thankfully, with medical imaging becoming one of the fastest growing data sources in medical sciences, we might soon reach a point where Image Analysis and Machine Learning will collectively help doctors in accurately predicting the early signs of a disease like Cancer or HIV in patients.