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Histatins were first described as anti-fungal agents in the saliva but have since been found to have anti-viral, anti-bacterial, wound healing and even anti-inflammatory activities 6 , 7. In whole saliva, the concentration of H1 without stimulation was Saliva of patients with rheumatoid arthritis with oral sicca symptoms has been noted to have decreased levels of histatins, and there has been interest in testing the use of histatins as markers of disease 11 — Recently, there has been some interest in the potential role of histatin peptides in the ocular surface and the lacrimal functional unit.

Some authors have found presence of expression of histatins in components of the ocular surface and on Schirmer tear strip samples from patients 14 — It was also demonstrated that H1 is present in epithelia of accessory lacrimal glands of humans, corneal and conjunctival epithelia, and that H1 can promote migration of human corneal epithelia 17 , H1 is thought to be able to promote epithelial migration, adhesion, barrier integrity and reduces the effects of epithelial-mesenchymal transition promoting agents Such characteristics, if ascribed to an intrinsic component of the tear film would be important to a healthy ocular surface.

Given the increasing evidence that histatin peptides are present in the ocular surface and tear film unit and the evidence that H1 can promote epithelial wound healing We sought to test two questions: Do the tears of normal patients contain H1? Do the tears of patients with ADDE have diminished levels of histatins?. We focused primarily on H1 as this is one of the most studied in saliva, has been demonstrated in the lacrimal functional unit, and could be an interesting tear component to replace due to its epithelial trophic functions 17 , These findings argue for routine reporting of metadata on potential patient, hospital system and preprocessing confounds.

By illuminating the construction of radiographic datasets in greater detail, these data will make it easier for domain experts to identify likely sources of confounding. Additionally, these metadata enable the construction of models that explicitly control for confounds, providing a route to AI systems that generalize well even in the context of confounded training data 32 , 33 , Alternative hypotheses do not explain poor generalization To verify the hypothesis that exploitation of dataset-specific confounding leads to poor generalization performance, we investigated alternative explanations for the generalization gap.

Previous publications have suggested that more complex models—that is, those with higher capacity—may be particularly prone to learning confounds 36 , so we evaluated the generalization performance of simpler models, including a logistic regression and a simple convolutional neural network architecture, but found that the generalization gap did not improve Supplementary Fig.

This result further supports the broad applicability of our findings, because the generalization gap was present regardless of network architecture, aligning with a previous study that showed that radiograph classification performance is robust to neural network architecture Although our saliency maps sometimes highlight the lung fields as important Fig.

The saliency maps frequently highlight laterality markers that originate during the radiograph acquisition process Fig. Reliance on such confounds, which do not consistently correlate with COVID status in outside datasets, helps explain the previously observed poor generalization performance. Top: in a COVIDnegative radiograph, in addition to the highlighting in the lung fields open arrow , the saliency maps also emphasize laterality tokens filled arrow.

Middle: in a COVIDpositive radiograph, the most intensely highlighted regions of the image are the bottom corners arrows , outside of the lung fields. The colour bar indicates saliency map pixel importances by percentile. Figure adapted with permission from ref. Winther et al. This technique should capture a broader range of features than saliency maps, as the GANs are optimized to identify all possible features that differentiate the datasets.

However, the generative networks frequently add or remove laterality markers and annotations Fig. The generative networks additionally alter the radiopacity of image borders Fig. Given this strong evidence that ML models can leverage spurious confounds to detect COVID, we also investigated the extent to which our classifiers, in particular, relied on the features altered by the GAN.

We found that images transformed by the GANs were reliably predicted by the classifiers to be the transformed class rather than the original class Supplementary Fig. Thus, the image transformations from the GANs enable us to see hypothetical versions of the same radiographs that would have caused our classifiers to predict the opposite COVID status. Experimental validation of factors identified by interpretability methods We next aimed to experimentally validate the importance of spurious confounds to our models by manually modifying key features Fig.

As a control, we compared to randomly swapped image patches of the same size and found that the change in model output from swapping laterality markers is significantly greater than expected by random Fig. Although these markers vary consistently between the datasets Fig. We similarly investigated the shoulder region of radiographs, which was often highlighted as an important feature in our saliency maps Supplementary Fig. To verify whether these findings held on a population basis, we sampled a random subset of the radiographs and repeated our experiments involving the swapping of laterality markers and movement of the shoulder region Supplementary Fig.

Grey dots in the distribution plots right correspond to the change in model output after swapping random image patches, which were used as a negative control. Red dots correspond to the change in model output for the radiographs with swapped laterality markers. Saliency maps highlight the shoulder region as important predictors of COVID positivity after but not before this region is moved to the top of the image left. Grey dots in the distribution plot right correspond to radiographs with randomly selected patches.

The red dot corresponds to the radiograph with the shoulder regions moved. Full size image Fig. Solid red boxes indicate systematic differences in laterality markers that are visible in the average images. Dashed red boxes indicate systematic differences in the radiopacity of the image borders, which could arise from variations in patient position, radiographic projection or image processing.

Full size image Shortcuts have a variable effect on generalization Importantly, some shortcuts will impair generalization performance, but other shortcuts will not. While the large generalization gap is explained well by shortcut learning, a portion of the remaining external test set performance may still be due to shortcuts that happen to generalize for our datasets.

Both types of shortcut are undesirable, because even those that generalize between our datasets may not consistently generalize to other settings, and the use of clinical rather than strictly radiological information extracted from these radiographs may be redundant, depending on the clinical workflow.

To analyse which shortcuts may contribute to poor generalization, we considered clinical metadata Supplementary Table 1 and average images from each repository Fig. In addition, the radiographic projection, which may contribute to but does not completely explain the importance of the image edges and shoulder position, does not generalize between the datasets Fig.

Among the shortcuts that do generalize at least between our datasets are aspects of patient positioning that do not result from the radiographic projection. These aspects of patient positioning also probably contribute to the previously observed importance of image edges and shoulder position, and they maintain a consistent relationship with COVIDnegative and COVIDpositive radiographs in each dataset Fig.

An additional factor that may generalize well is patient sex, because, within both datasets, a higher proportion of males were COVIDpositive Supplementary Table 1. Given that radiographic projection and patient sex are diffusely represented in radiographs and therefore less clearly pointed out by our explainability approaches, we also validated whether our models could leverage these factors as shortcuts.

We reasoned that, for a model to be able to leverage these concepts as shortcuts, the same model when retrained must be able to predict these concepts well. Indeed, our models accurately predict both the radiographic projection and patient sex for both internal and external test data Fig.

Models were trained to predict radiographic projection AP versus PA view or patient sex and then evaluated on internal and external test radiographs. Full size image Improved data mitigate shortcut learning Given this strong evidence that neural networks leverage dataset-level differences as shortcuts for COVID status, we enquired to what extent this issue might be mitigated.

Although an initial hypothesis may be that the choice of neural network architecture determines the propensity for shortcut learning, all architectures that we examined displayed similar evidence for shortcut learning, as quantified by the generalization performance Supplementary Fig. Although our tests hinted that data augmentation may help alleviate shortcut learning, the effect was small and not statistically significant Supplementary Fig.

In principle, an attractive solution to mitigate shortcut learning is to remove the image factors that the models leverage as shortcuts. However, in practice, it is difficult to remove all such image factors. After retraining our models on these cropped radiographs, we found that such cropping does not improve generalization performance Supplementary Fig. However, considering the consistent identification of this factor by saliency maps, the CycleGANs and manual image modifications Figs.

Conjecturally, such image attributes could include the size of the lung fields relative to the image, the positioning of the scapular shadows, the size of the cardiac silhouette, image intensities or textural features that enable inference of the data source.

Perhaps a more reliable solution to remove the image factors that enable shortcut learning is to simply collect data that is less confounded. Furthermore, saliency maps for the model trained on dataset III tend to attribute more importance to the lung fields, where COVID pathology would be expected, than to potentially confounding regions, as compared to the equivalent saliency maps generated for the model trained on dataset II Supplementary Fig.

Taken together, these findings argue for careful collection of data so as to minimize the potential for shortcut learning, with continued caution that improved data collection may only partially solve the problem. Full size image Discussion ML models that were built and trained in the manner of recent studies generalize poorly and owe the majority of their performance to the learning of shortcuts.

This undesired behaviour is due partially to the synthesis of training data from separate datasets of COVIDnegative and COVIDpositive images, which introduces near worst-case confounding and thus abundant opportunity for models to learn these shortcuts. Previous studies also audited AI systems for the detection of COVID in radiographs, with mixed success at identification of shortcuts. In a simple yet clever approach, one study found that models retain high performance when examining only the borders of radiographs, such that genuine COVID pathology was removed from the images This study concurs with our findings but comments primarily on the possibility of this issue rather than its occurrence in the wild, though it is nonetheless alarming.

A number of other studies that involve datasets with severe confounding between pathology and image source 3 , 5 , 6 , 7 , 8 similarly audit their models using saliency map approaches most prominently, the Grad-CAM approach 43 and report findings on one to three radiographs, without noting evidence of shortcut learning. Based on this pattern, we recommend that researchers examine and report results from explainable AI or saliency map approaches on a population level, employing a sampling-based approach as necessary, and to remain sceptical of high performances in the absence of external validation.

Moreover, we find that population-level audits using saliency maps are highly labour-intensive to perform in a rigorous manner and may depend on domain knowledge, which motivates future approaches for explainable AI in medical imaging that simplify population-level analysis.

Our findings support common-sense solutions to alleviate shortcut learning in AI systems for radiographic COVID detection, including 1 improved collection of training data, that is, data in which radiographs are collected and processed in a way matching the target population of a future AI system and 2 improved choice of the prediction task to involve more clinically relevant labels, such as a numeric quantification of the radiographic evidence for COVID 27 , However, we demonstrate that shortcut learning may occur even in a more ideal data collection scenario, highlighting the importance of explainable AI and principled external validation.

Although AI promises eventual benefits to radiologists and their patients, our findings demonstrate the need for continued caution in the development and adoption of these algorithms 9. Methods Model architecture and training procedure For our primary neural network, we used a convolutional neural network with the DenseNet architecture to predict the presence versus absence of COVID This architecture has not only been used in a variety of recent models for COVID classification 4 , 5 , but has also been used for the diagnosis of non-COVID pneumonia 34 , 39 , as well as for more general radiographic classification Following the approach in recent COVID models 4 , 5 , we first pre-trained the model on ImageNet, a large database of natural images Forcing models to first learn general image features should also serve as an inductive bias to prevent overfitting on domain-specific features After ImageNet pre-training, the final 1,node classification layer of the trained ImageNet model was removed and replaced by a node layer, corresponding to the 14 pathologies recorded in the ChestX-ray14 dataset plus an additional node corresponding to COVID pathology.

Only the prediction for COVID was used for evaluating the model, but we followed previous works that showed simultaneous learning of multiple tasks was useful for achieving the highest predictive performance The model was optimized end to end using mini-batch stochastic gradient descent with a batch size of 16, momentum parameter of 0.

We chose a binary cross-entropy loss as the optimization criterion. All models were trained for 30 epochs, which was long enough for all models to reach a maximum in the validation AUC. All models were trained using the PyTorch software library 47 , version 1. We also examined three architectures that were designed in previous publications specifically for the task of COVID detection, with the hypothesis that these specialized architectures may better learn genuine COVID pathology and generalize better to external data.

We trained these models on datasets I and II, following the image preprocessing procedures, data augmentation pipelines and optimization schemes used in the original publications we note that although dataset I is analogous to the original datasets used to train DarkCovidNet and COVID-Net, CVNet was trained on data that are not publicly available. For both CVNet and DarkCovidNet, the base architectures were downloaded from the torchvision library 47 , then modified to match the descriptions in each respective paper.

For the CVNet paper, the data augmentation pipeline was altered to match the pipeline in the original paper: when loading images, each radiograph is additionally randomly flipped with probability 0. To disentangle performance differences due to the ensembling present in the CVNet architecture from performance differences due to the change in data augmentation, we also trained a single DenseNet model with the same data augmentation steps as CVNet.

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