Deep neural networks show impressive performance in medical imaging tasks. However, many current networks generalise poorly to data unseen during training , for example data generated by different medical centres . Such behaviour can be caused by networks overfitting easy-to-learn, or statistically dominant, features while disregarding other potentially informative features. Moreover, dominant features can lead to learning spurious correlations. For instance , indistinguishable differences in the sharpness of the images from two different scanners can degrade the performance of the network significantly.  To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty . Simulation experiments show that spectral decoupling allows training neural networks on datasets with strong spurious correlations. Networks trained without spectral decoupling do not learn the original task and appear to make false predictions based on the spurious correlations. Spectral decoupling also significantly increases networks' robustness for data distribution shifts. To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer on haematoxylin and eosin stained whole slide images. The networks are then evaluated with data scanned in the same medical centre with two different scanners, and data from a different centre . Networks trained with spectral decoupling increase the accuracy by 10 percentage points over weight decay on the dataset from a different medical centre . Our results show that spectral decoupling allows training robust neural networksto be used across multiple medical centres, and recommend its use in future medical imaging tasks .
Many current neural networks for medical imaging generalise poorly to data unseen during training . Such behaviour can be caused by networks overfitting easy-to-learn, or statistically dominant, features while disregarding other potentially informative features. For example , indistinguishable differences in the sharpness of the images from two different scanners can degrade the performance of the network significantly. All neural networks intended for clinical practice need to be robust to variation in data caused by differences in imaging equipment, sample preparation and patient populations. To address these challenges, we evaluate the utility of spectral decoupling as an implicit bias mitigation method . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty , thus having no added computational costs. We show that spectral decoupling allows training neural networks on datasets with strong spurious correlations. Networks trained without spectral decoupling do not learn the original task and appear to make false predictions based on the spurious correlations. Spectral decoupling also increases networks' robustness for data distribution shifts. To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer tissue slides and COVID-19 in chest radiographs . Networks trained with spectral decoupling achieve substantially higher performance on all evaluation datasets . Our results show that spectral decoupling helps with generalisation issues associated with neural networks. We recommend using spectral decoupling as an implicit bias mitigation method in any neural network intended for clinical use .
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Deep neural networks show impressive performance in medical imaging tasks. However, many current networks
Many current neural networks for medical imaging
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, for example data generated by different medical centres
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Moreover, dominant features can lead to learning spurious correlations. For instance
For example
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All neural networks intended for clinical practice need to be robust to variation in data caused by differences in imaging equipment, sample preparation and patient populations.
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in the context of medical image classification
as an implicit bias mitigation method
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. Simulation experiments
, thus having no added computational costs. We
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significantly
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on haematoxylin and eosin stained whole slide images. The networks are then evaluated with data scanned in the same medical centre with two different scanners, and data from a different centre
tissue slides and COVID-19 in chest radiographs
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increase the accuracy by 10 percentage points over weight decay on the dataset from a different medical centre
achieve substantially higher performance on all evaluation datasets
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allows training robust neural networksto be used across multiple medical centres, and recommend its use in future medical imaging tasks
helps with generalisation issues associated with neural networks. We recommend using spectral decoupling as an implicit bias mitigation method in any neural network intended for clinical use