The next step, say the researchers, is to extend the model for multiclass classification of breast cancer to detect cancer subtypes in addition to classifying benign and malignant tissues.
Breast cancer is one of the most common forms of cancer with, according to the CDC, the fourth highest mortality rate of all cancers as of 2019. But a new machine learning model has given healthcare providers a new weapon in the battle to defeat it.
Developed by researchers at Michigan Technological University, the new tool uses probability to classify more accurately instances of breast cancer revealed in histopathology images.
“Any machine learning algorithm that has been developed so far will have some uncertainty in its prediction,” explained Ponkrshnan Thiagarajan, a mechanical engineering grad student and one of the authors on a paper published in the journal IEEE Transactions on Medical Imaging. “There is little way to quantify those uncertainties. Even if an algorithm tells us a person has cancer, we do not know the level of confidence in that prediction.”
In the paper, Thiagarajan, along with fellow grad student Pushkar Kharinar and Susanta Ghosh, assistant professor of mechanical engineering and machine learning expert, explain how their novel probabilistic machine learning model can evaluate the uncertainty in its predictions as it classifies benign and malignant tumors, thus helping reduce the risk of false predictions.
In medical situations, not knowing how confident an algorithm is makes it difficult to rely on computer-generated predictions. According to the team, the new model is an extension of the Bayesian neural network — a machine learning model that can evaluate an image and produce an output. “We have developed a novel technique to utilize the uncertainties provided by the Bayesian–CNN that significantly improves the performance on a large fraction of the test data (about 6% improvement in accuracy on 77% of test data),” the team write in their report.
The model differentiates between negative and positive classes by analyzing the images, which at their most basic level are collections of pixels. In addition to this classification, the model can measure the uncertainty in its predictions. In a medical laboratory, such a model promises time savings by classifying images faster than a lab tech. And, because the model can evaluate its own level of certainty, it can refer the images to a human expert when it is less confident.
According to Thiagarajan, the idea for the model came when he started using machine learning to reduce the computational time needed for mechanical engineering problems. Whether a computation evaluates the deformation of building materials or determines whether someone has breast cancer, it’s important to know the uncertainty of that computation — the key ideas remain the same.
“Breast cancer is one of the cancers that has the highest mortality and highest incidence,” Thiagarajan said. “We believe that this is an exciting problem wherein better algorithms can make an impact on people’s lives directly.”
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