Deep Learning Medical Imaging For Disease Diagnosis

As artificial intelligence (AI) becomes increasingly used for critical applications such as diagnosing and treating diseases, predictions and results regarding medical care that practitioners and patients can trust will require more reliable deep learning medical imaging models.

A team of computer scientists proposes a novel deep learning approach aimed at improving the reliability of classifier models designed for predicting disease types from diagnostic images, with an additional goal of enabling interpretability by a medical expert without sacrificing accuracy. The approach uses a concept called confidence calibration, which systematically adjusts the model’s predictions to match the human expert’s expectations in the real world.

In practice, quantifying the reliability of machine-learned models is challenging, so the researchers introduced the “reliability plot,” which includes experts in the inference loop to reveal the trade-off between model autonomy and accuracy. By allowing a model to defer from making predictions when its confidence is low, it enables a holistic evaluation of how reliable the model is.

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