AI matches human experts in classifying microscopic organisms

Foraminifera (forams) are shelled microorganisms that are abundant in the Earth’s seabed. Analyzing different species of forams provides important information about climate change, the state of the marine environment, and suitable areas for carbon capture and storage.

Past research has attempted to automate these classification tasks—a usually laborious and time-consuming manual process—with deep learning (DL) methods. Several studies show significant promise, but few have focused on the uncertainty of the methods’ classifications.

“Uncertainty estimation is crucial to avoid misclassifications that could overlook rare and ecologically significant species, says Iver Martinsen, PhD Candidate at UiT The Arctic University of Norway and SFI Visual Intelligence (VI). “It is important to develop DL methods which accurately calculate how uncertain their predictions are.”

In a recently published study in Artificial Intelligence in Geosciences, Martinsen and researchers at UiT, VI, Nofima, and NSE show how deep learning can achieve human-level performance in estimating uncertainty when classifying forams. “Using 260 images of forams and sediment grains, we trained the DL methods to detect and classify these microscopic organisms,” says Martinsen.

Evaluating the performance of such methods remains a challenging. To address this, the researchers created a human-derived set of uncertainty estimations based on classification task responses from four senior geoscientists.

“The geoscientists were given the same 260 images and were tasked to classify each of them, as well as state their confidence level. This formed a comparative baseline which allowed us to assess the models’ estimations to those of human experts,” Martinsen explains.

The study also demonstrates how human uncertainty estimations may provide a relevant and valuable baseline for comparison, he adds. Results show that the DL methods’ estimations can match—and at times be better than—expert geoscientists.

“We gain valuable insights on how these methods’ estimations compare to each other and human experts. We believe this research is a leap towards making these automated tools more reliable, trustworthy, and applicable in real-world settings,” Martinsen says.

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Contact the author: 

Iver Martinsen, PhD Candidate at SFI Visual Intelligence: iver.martinsen@uit.no

Petter Bjørklund, Communications Officer at SFI Visual Intelligence: petter.bjorklund@uit.no

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