A Michigan Tech-developed machine learning model uses probability to more accurately classify breast cancer shown in histopathology images and evaluate the uncertainty of its predictions.

The test images are divided into three subsets. Images with: 11 a) low uncertainty 11 b) medium uncertainty and 11 c) high uncertainty. A dimensionality reduction of the images reveals that the images with low uncertainty (11 a) show clear distinction between the benign and malignant images. These are the images with low uncertainty are easily separable in low dimensions and the machine learning model is confident in classifying these images. Whereas the images with high uncertainty are randomly distributed in three dimensions (11 c). For medium uncertainty images, the images are clustered without a clear distinction of classes. Thus, explaining the uncertainty quantified by the machine learning model. (Courtesy Ponkrshnan Thiagarajan/Michigan Tech)

December 8, 2021 — A Michigan Tech-developed machine learning model uses probability to more accurately classify breast cancer shown in histopathology images and evaluate the uncertainty of its predictions.

Breast cancer is the most common cancer with the highest mortality rate. Swift detection and diagnosis diminish the impact of the disease. However, classifying breast cancer using histopathology images—tissues and cells examined under a microscope—is a challenging task because of bias in the data and the unavailability of annotated data in large quantities. Automatic detection of breast cancer using convolutional neural network (CNN), a machine learning technique, has shown promise; however, it is associated with a high risk of false positives and false negatives.

Without any measure of confidence, such false predictions of CNN could lead to catastrophic outcomes. A new machine learning model developed by Michigan Technological University researchers, however, can evaluate the uncertainty in its predictions as it classifies benign and malignant tumors, helping reduce this risk.

In a paper recently published in the journal IEEE Transactions on Medical Imaging, mechanical engineering graduate students Ponkrshnan Thiagarajan and Pushkar Kharinar and Susanta Ghosh, assistant professor of mechanical engineering and machine learning expert, outline their novel probabilistic machine learning model, which outperforms similar models.

“Any machine learning algorithm that has been developed so far will have some uncertainty in its prediction,” Thiagarajan said. “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.”

From Experience Comes Confidence

In the medical context, not knowing how confident an algorithm is has made it difficult to rely on computer-generated predictions. The present model is an extension of the Bayesian neural network—a machine learning model that can evaluate an image and produce an output. The parameters for this model are treated as random variables that facilitate uncertainty quantification. 

The Michigan Tech 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.

But why is a mechanical engineer creating algorithms for the medical community? Thiagarajan’s idea kindled 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.”

Next Steps

Now that the study has been published, the researchers will extend the model for multiclass classification of breast cancer. They will aim to detect cancer subtypes in addition to classifying benign and malignant tissues. And the model, though developed using breast cancer histopathology images, can also be extended for other medical diagnoses.

“Despite the promise of machine learning-based classification models, their predictions suffer from uncertainties due to the inherent randomness and the bias in the data and the scarcity of large datasets,” Ghosh said. “Our work attempts to address these issues and quantifies, uses and explains the uncertainty.”

Ultimately, Thiagarajan, Khairnar and Ghosh’s model itself—which can evaluate whether images have high or low measures uncertainty and identify when images need the eyes of a medical expert—represents the next steps in the endeavor of machine learning.

For more information: www.mtu.edu

Related Content

News | Radiology Business

January 21, 2022 — IBM and Francisco Partners, a leading global investment firm that specializes in partnering with ...

Time January 21, 2022
Feature | Breast Imaging | By Bhvita Jani

The rising global incidence rates of breast cancer, coupled with the severe backlog of women requiring breast cancer ...

Time January 20, 2022
News | Artificial Intelligence

January 20, 2022 — Leading health tech firm Qure.ai has gained 510(k) clearance from the Food and Drug Administration ...

Time January 20, 2022
Feature | Artificial Intelligence | By Dave Fornell, ITN Editor

Artificial intelligence (AI) has found a unique niche to help automate the activation of acute care teams for pulmonary ...

Time January 19, 2022
Sponsored Content | Case Study | Ultrasound Imaging

The most common cause of chronic liver disease? Nonalcoholic fatty liver disease (NAFLD). With 25% of the world’s ...

Time January 19, 2022
News | Breast Imaging

January 18, 2022 — UE LifeSciences has entered into a definitive distribution agreement with Siemens Healthineers ...

Time January 18, 2022
News | Ultrasound Women's Health

January 17, 2022 — According to an article in ARRS’ American Journal of Roentgenology (AJR), radiologists should ...

Time January 17, 2022
News | Coronavirus (COVID-19)

January 14, 2022 — The COVID-19 pandemic took the world by storm in early 2020 and has become since then the leading ...

Time January 14, 2022
News | Mammography

January 14, 2022 — Computer engineers and radiologists at Duke University have developed an artificial intelligence ...

Time January 14, 2022
Videos | Artificial Intelligence

Here are two examples of artificial intelligence (AI) driven pulmonary embolism (PE) response team apps featured by ...

Time January 13, 2022
Subscribe Now