DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations

Authors: nima  fathi
amar  kumar
brennan  nichyporuk
mohammad  havaei
Venue: MIDL_io_2024_Conference
Published: 2024-01-31 23:50:29

Abstract: Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability.  Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.

Main paper: 
 introduction: 
 Deep learning (DL) methods have shown tremendous success in a wide variety of medical image tasks, including disease classification, due to their ability to learn generalizable, discriminative features across subjects. However, DL models are prone to learning shortcuts in order to obtain high overall accuracies, including any prevalent visual artifacts (e.g. marks in the image (DeGrave et al., 2021) ) that are correlated with, but not causal of, the target outcome. Models that have not learned the relevant causal visual markers (Jia and Liang, 2017; Zech et al., 2018) are right for the wrong reasons (Sun et al., 2023b,a) , and fail to generalize across out-of-distribution subgroups (Geirhos et al., 2018 (Geirhos et al., , 2020)) . Explainable DL models that not only expose these biases but mitigate them are required in order to ensure their trustworthiness for safe clinical deployment.Counterfactual (CF) image generation methods (e.g. Gifsplanation (Cohen et al., 2021 ) and Attri-Net (Sun et al., 2023b,a) ) have recently been successful at exposing when the classifier is latching onto spurious correlations in order to obtain high performance. These methods employ conditional generation of the counterfactual image when the classifier has the opposing target outcome. Differences between the factual and counterfactual images should reflect the predictive local markers indicative of the class label, but also expose the classifier's reliance on spurious correlations. These methods do not mitigate the biases nor address their poor generalization. A number of debiasing methods (Sagawa et al., 2020; Wang et al., 2020; Sarhan et al., 2020) have recently been successful in several medical imaging contexts. Recent work (Kumar et al., 2023) combined Cycle-GAN counterfactual image generation and a Group-DRO (Sagawa et al., 2020) classifier to expose and mitigate the biases. The results showed improvement for minority subgroups, and classification based on disease-specific features. However, debiasing techniques have a number of known drawbacks, including improved fairness at the expense of a reduction in the performance in the majority subgroup. Integrating them into the counterfactual models requires sub-group labels for each class during training (which is often unavailable), and GANs require retraining the generative model with the pre-trained debiasing classifier in order to provide supervision for the counterfactual synthesis, which makes the process inflexible. Recently, an unconditional DDPM (Denoising Diffusion Probabilistic Model) (Ho et al., 2020) , DiME (Jeanneret et al., 2022) , has been proposed to generate classifier-guided counterfactual explanations, however, with the classifier latching onto shortcuts present in the dataset. Overall, developing bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse visual artifacts remain open research questions.This paper introduces DeCoDEx, a diffusion-based (DDPM) counterfactual image generator for debiased classifier explainability in the presence of dominant and diverse visual artifacts. DeCoDEx overcomes a number of limitations of current approaches: Rather than requiring training specialized debiasing classifiers for known subgroups, and then retraining the counterfactual generator that uses them (e.g. GANs), the framework provides debiased explainability of the classifier in question at inference time by leveraging the flexibility and explicit inference procedure (Dhariwal and Nichol, 2021; Wang et al., 2022; Kazerouni et al., 2022) of DDPMs, is generalizable to any subgroups, and shows stable training as compared to GANs. The framework can make use of any pre-trained, binary detector trained to indicate the presence or absence of the visual artifact in question (for any classes). During inference, the detector guides the diffusion-based counterfactual image generator towards accurate explainability, as the gradients from the detector counter the gradients from the classifier away from spurious correlations.Extensive experiments are performed on the publicly available CheXpert dataset (Irvin et al., 2019) , using both synthetic artifacts and real visual artifacts (support devices). Qualitative results show that the proposed method successfully synthesizes the counterfactual images by making changes in the pathology associated with Pleural Effusion while preserving or ignoring the visual artifacts. The quality of the counterfactual images was measured via several metrics, such as L1 distance, Counterfactual Prediction Gain (CPG) and Spuri-ous Correlation Latching Score (Kumar et al., 2023) . The results indicated the strength of framework over a baseline (without a detector). Augmentation of the dataset with counterfactual images synthesized with DeCoDEx improves ERM and Group-DRO classification for the minority subgroups. 
methodology: 
 The DeCoDEX framework involves a training strategy for explainability via counterfactual image synthesis, while ensuring robustness to spurious correlations. The model consists of a classifier and a trained unconditional denoising diffusion probabilistic model (DDPM). The framework leverages a pre-trained visual artifact detector that guides a DDPM to synthesize counterfactuals while ignoring the artifact. One of the advantages of our approach is the flexibility to use any pre-trained detector that can identify spurious correlations in the input sample. During the counterfactual image generation, when the biased classifier relies on spurious non-causal features in the dataset, the detector's gradient reversal signal readjusts the generation process, steering it back toward focusing on relevant pathological features (Fig. 1 ). It should be noted here that if there are several spurious correlations in the dataset that are difficult to detect, the detector may only block only some of them. Therefore, for some difficult cases, even after using a detector the counterfactual images may fail to make changes in the area correlated with the target class. An overview of our method is shown in Fig. 2 . Counterfactual Image Generation: The counterfactual image generation is designed to adhere to a set of constraints (Mothilal et al., 2020; Nemirovsky et al., 2020) through the following loss functions: (i)Identity preservation loss, L perc : counterfactual images preserves the identity of the factual image, L perc ; (ii) Classifier consistency loss, L class : counterfactual image belongs to the correct target class. An additional Detector loss term, L det , is intro- duced to help guide the generation away from the spurious correlations that the classifier would have otherwise latched onto.The generative model for synthesizing counterfactual images use a pre-trained DDPM with classifier guidance. DDPMs operate through a forward diffusion process (Eq. 1), which incrementally corrupts the original image x (or z 0 ) by adding Gaussian noise, culminating in a highly noised image z T over T timesteps.EQUATIONwhere β t are predefined noise levels. The reverse diffusion process (Eq. 2) aims to recover the original image from z T , using a neural network to predict and subtract the added noise iteratively.EQUATIONwhere α t := t k=1 (1 -β k ) and ϵ θ (x t , t) is the noise estimated by the network at step t. Guided-diffusion sampling (Dhariwal and Nichol, 2021) is used to denoise the image at any time step t, given by z t-1 ∼ N (µ(z t ) -Σ(z t )∇ zt L(z t ; y c , y s , x t ), Σ(z t )). The complete loss function is given by:EQUATIONwhere C and D refer to our classifier and detector modules, y c and y s refer to the target labels of the class and the presence of spurious correlation and λ c , λ d , λ p are hyperparmeters. Finally, the gradient of the complete loss function can be expressed as follows:EQUATION 
dataset and implementation details: 
 We perform experiments on the publicly available CheXpert dataset (Irvin et al., 2019 ) that contains over 200,000 chest X-ray images, with binary labels for 14 diseases (e.g. Pleural Effusion, Cardiomegaly, Pneumonia), as well as binary labels indicating the presence of support devices (visual artifacts). We create two variants of the CheXpert dataset: (i) Dot dataset: We introduce a synthetic artifact, a black dot of radius 9 pixels, in the center of the image for evaluating the quality of counterfactual images in the presence of spurious correlations. This also helps to compare the behaviour of counterfactual images synthesized from the baseline method: DeCoDEx without a detector and the proposed DeCoDEx. (ii) Device dataset: A subset of the CheXpert data is used to demonstrate the performance of DeCoDEx in the presence of real artifacts (Support devices). In both datasets, the artifacts are present in the majority of images of subjects with Pleural Effusion and in the minority of images of healthy subjects. In contrast, the majority of the images of healthy subjects and the minority of the images of subjects with Pleural Effusion do not contain these artifacts. Fig. 3 shows the sample images from both the datasets and all four subgroups. For both datasets, the ratio of the number of samples in majority to minority is 90:10 and the dataset is divided into training/validation/testing with a 70/15/15 random split. The details of number of samples in different split is included in Appendix A.The DenseNet-121 (Huang et al., 2017) architecture is used to train the classifier and detector. The classifier is trained separately on Dot and Device datasets. We use the standard Empirical Risk Minimization (ERM) (Sagawa et al., 2020) as the optimization Several metrics are used to evaluate the quality of counterfactual images: (i) Subject Identity Preservation: L1 Score as depicted by the L1 distance between the counterfactual and the factual (original) image (as in (Mothilal et al., 2020; Nemirovsky et al., 2020) ); (ii) Counterfactual Prediction Gain (CPG) (Nemirovsky et al., 2020) which measures the absolute value of the difference in the prediction of the classifier on the factual and counterfactual images (a higher score indicates a maximal change in the classifier decision boundary); (iii) Spurious Correlation Latching Score (SCLS)) (Kumar et al., 2023) assesses whether the spurious correlation was preserved in the counterfactual image (a lower SCLS score is desirable); (iv) Classifier Flip Rate (CFR) which represent the number of samples that flipped their class as per the classifier and (v) Detector Robustness Rate (DRR) showing number of samples that were robust to the detector.In order to show that the synthesized counterfactuals learn useful features associated with the disease, the training data for the original classifier was augmented with synthesized images. The ERM classifier is retrained with the augmented data. An increase in performance indicates that synthesized images learned discriminative features generalizable to the 0. Only the training dataset is augmented while the validation and test split remains the same. subgroups. We augment the Dot dataset with 200 counterfactual images and the Device dataset with 600 counterfactual images synthesized using basline method and DeCoDEx. For completeness, the augmentation experiments are repeated for a debiasing Group-DRO classifier.Classifier and Detector evaluation The classifier's performance on both datasets is shown in Table 2 on the row labeled 'ERM'. Note that the performance on the minority subgroup samples is significantly lower than the majority for both datasets. The dot and device detectors perform very well with subgroups [majority S , minority S , minority H , majority H ] accuracies of [100, 99.9, 100, 99.8] and [91.8, 88.8, 79.2, 88.7] . Perfect accuracies for the dot detector can be expected given that the position and size of the dot remain fixed for all the subjects. However, the variability including size, position, intensity of support devices can be large, making detection much more challenging.Qualitative evaluation Pleural effusion (PE) is characterized by the rounding of the costophrenic angle, augmented lung opacity, and reduced clarity of the diaphragm and lung fissures (Light, 2002) . It is observed in the lower corner of the lungs (Wang et al., 2017; Huang et al., 2022) . Qualitative results for counterfactual generation from the models can be seen in Fig. 4 . For the baseline method on the Dot dataset, the counterfactual for a healthy subject simply added dots to the image, indicating that the classifier has latched onto the dot artifact. However, DeCoDEx ignored the dot artifact (and maintained the original text artifact) and made changes reflective of PE disease. For the Device dataset, the baseline generated a counterfactual for a sick patient from the majority subgroup by simply removing the support device(s) while DeCoDEx makes (correct) changes in the area associated with the disease. Therefore, DeCoDEx indeed ignores the spurious correlation. The best results are in bold. Note the increase in performance for the minority classes when both the methods, ERM and Group-DRO are augmented with De-CoDEx counterfactual images, illustrating the power of our method on extracting disease pathology and generating better counterfactual explanations.groups for the Dot dataset, indicating that CF samples have learned discriminative features common to this subgroup. Among both ERM and Group-DRO based techniques, our method outperforms the Baseline along with augmented CFs. CF synthesized from DeCoDEx when augmented with ERM performs as well as Group-DRO for some minority classes which is a positive result, particularly when debiasing cannot be performed due to the lack of annotations. 
conclusions: 
 In medical image analysis, explainable models are needed to expose and mitigate the bias to improve the trustworthiness of complex models. This paper presents DeCoDEx, an explainability framework that leverages a pre-trained classifier and detector to guide diffusion-based counterfactual synthesis towards accurate disease markers while ignoring spurious correlations. Qualitative and quantitative analysis of our extensive experiments indicate that the proposed method outperforms the baseline model that does not use a detector. Furthermore, the flexibility of our method allows it to be used with any pre-trained detector, does not require retraining a debiasing classifier and associated generative architecture, and provides guidance during inference. One of the current limitations of the model is resulting minor changes throughout the generated CF images. Future work will explore conditional score-based generative models. 
 
 Appendix:  


Decision: One reviewer did not update their recommendation and, from my appreciation of their concerns and the response, I am assuming they would be only partially satisfied as the response addresses the issue pertaining to ablations but does not address the potential weaknesses of minor changes of the counterfactuals and complex dependencies. Considering the overall positivity of this review, however, I will assume that this shortcoming would not result in a dramatically lower verdict. Thus, after revision, there is reviewer consensus that the manuscript can be accepted. The additional experiments should be included in the final manuscript.

Reviews: review: paper_summary: paper_summary: This paper presents a novel framework aimed at improving the explainability of deep learning classifiers in medical imaging. It specifically addresses the issue where classifiers rely on spurious, non-causal features (confounders) in datasets, leading to biased predictions. The proposed DeCoDEx framework leverages a pre-trained binary artifact detector during inference to guide the synthesis of counterfactual images by a diffusion-based generator, focusing on causal features and ignoring irrelevant artifacts. The methodology involves generating counterfactuals while preserving subject identity and ensuring classifier consistency, using a denoising diffusion probabilistic model (DDPM) guided by the artifact detector. Extensive experiments on the CheXpert dataset, using both synthetic and real artifacts, demonstrate that DeCoDEx improves the quality of counterfactual explanations and classifier performance, particularly for underrepresented groups. The paper suggests that integrating DeCoDEx with existing classifiers can enhance bias mitigation and model trustworthiness without requiring retraining or subgroup labels.
main_review: main_review: In general, this is a successful application of deep neural networks (diffusion models) to medical imaging. There are novelty proposed in the model structure, and the model out-performs other recently published baselines. Although there are detects in the model framework, they cannot be the reason to deny the advantage of the proposed model. So, I recommend to accept this paper after minor revision.
strength_weakness: strength_weakness: The paper identifies a limitation within its proposed DeCoDEx framework, namely that it results in minor changes throughout the generated counterfactual (CF) images. This could potentially limit the interpretability and granularity of the explanations provided by the model. Additionally, the paper mentions that future work will explore conditional score-based generative models, which implies that the current model may not fully capture or manipulate the complex dependencies or conditions present in medical imaging data​. Finally, there is no ablation study in this paper. I suggest that the authors provide an ablation study to further discover which part of the model contributes most to the performance. In this way, people can understand the proposed DeCoDEx better intuitively.
questions: questions: Not applicable
paper_summary: paper_summary: The authors present a method to generate counterfactual images aiming to elucidate if a black-box classifier jumped on a confounder or the actual target. For this, they set up a training method for the generative model, and conduct extensive experiments to show the feasibility of their approach.
main_review: main_review: I think this submission inspires. Besides its clear presentation, it deals with a subject of great importance in the medical field, has potential for application in other medical domains and not least provides a code basis (which is not yet documented, though).
strength_weakness: strength_weakness: I'm unfortunately late with my reviews, so I have to focus on the questions of greatest concern. They are few, anyways.
The main discussion point seems to be for me the artifact-specific classifier that needs to be provided for the method to work. This implies that I know of all artifacts that could confuse the method, and that I have a way of labeling the data accordingly. If there are multiple such artifacts, I wonder how I should proceed: Train one classifier? Or will I need to extend the method to have one loss per detected artifact? Or move from a binary to a n-ary classifier and a more complicated loss? I'd like to raise this topic for the rebuttal.
Another point was the role of the classifier and the detector in the generation pipeline. Is it correct that the classifier is the "given" entity for which the explanation should be achieved, by using the artifact detector to generate the counterfactual image -- and the wrong classifier decision makes the detector kick in to drive the generative process? I think the thought model behind this setup could use a few more words.
questions: questions: See above.
paper_summary: paper_summary: This paper proposes DeCoDEx, a diffusion based framework to generate counterfactuals while ignoring visual artefacts. The framework involves a DDPM, a pre-trained classifier for disease, and a pre-trained detector for visual artefacts. The main idea is to generate counterfactuals with the guide of gradients from the classifier and detector to change/maintain disease area while ignoring artefacts.
main_review: main_review: This paper leverages DDPM, a pre-trained disease classifier and a pre-trained artefact detector to produce counterfactuals that ignore artifact. Although the technical novelty is limited compared to diffusion works, I think the application is clever and the problem it tries to solve is significant.

=====update======
The response addresses my concern about novelty. They also performed experiments to check if unintervened attribute remain the same.
strength_weakness: strength_weakness: - The term “counterfactual” is overused in the field of image synthesis. Speaking in terms of causality theory, to produce a counterfactual, we need to define a causal graph and to abduct the background noises, such that all unrelated attributes remain the same. In this paper, the authors used identity preservation loss, however it may not guarantee that all unrelated attributes do not change. For instance, if we assume disease is not causally related to gender, then when we change disease, gender should not change as it is not affected. This is not discussed in the paper. 
- The proposed method requires pre-trained disease classifier and pre-trained artefacts detectors, which are not always feasible. Have you consider using classifier-free guidance to get rid of reliance on pre-trained classifiers?
- The technical contribution/novelty of this paper is not clear compared to diffsuion works (e.g. Ho et al.). The benefit of "the flexibility of our method allows it to be used with any pre-trained detector” is actually from diffusion models. This paper is more like an application of classifier-guided diffusion sampling, not an improvement.
questions: questions: -In the paper, it is stated the previous diffusion works "with the classifier latching onto shortcuts present in the dataset”. How do the authors ensure the pre-trained disease classifier is not affected by shortcuts? 
- See “Weakness".

Comments: comment: Thank you. We have included the details in the _Appendix D_ of the manuscript.
Thank the authors for the response. I update my rating accordingly. Congratulations on the great work.

"We did run experiments whereby a classifier was built to identify the sex of the factual subject. We checked and indeed, the sex attribute remained stable for all real and counterfactual images in all cases. We can add these experiments to the Appendix if needed.”

Yes, please. If space allows, why not.
Thank you for your valuable and detailed comments. We are glad that you found our paper well-written and extensively researched.

>..it is stated the previous diffusion works "with the classifier latching onto shortcuts present in the dataset”. How do the authors ensure the pre-trained disease classifier is not affected by shortcuts?

We wish to reiterate that we do not claim the pre-trained disease classifier is not affected by shortcuts. In fact, it is quite the opposite. Our framework provides a counterfactual explanation method that ignores spurious correlations and, instead, explains the decision boundary of the classifier through the guidance of the detector. 

>The term “counterfactual” is overused in the field of image synthesis. Speaking in terms of causality theory, to produce a counterfactual, we need to define a causal graph and to abduct the background noises, such that all unrelated attributes remain the same. In this paper, the authors used identity preservation loss, however it may not guarantee that all unrelated attributes do not change. For instance, if we assume disease is not causally related to gender, then when we change disease, gender should not change as it is not affected. This is not discussed in the paper.

We agree with the reviewer about the field of image synthesis, but addressing this point is beyond the scope of our current work. It is true that identity preservation loss does not guarantee that all the non-causal attributes remain the same. However, we do not have complete information about all the causal/ non-causal factors directly related to the disease under observation. We did run experiments whereby a classifier was built to identify the sex of the factual subject. We checked and indeed, the sex attribute remained stable for all real and counterfactual images in all cases. We can add these experiments to the Appendix if needed. 

>The proposed method requires pre-trained disease classifier and pre-trained artefacts detectors, which are not always feasible. Have you consider using classifier-free guidance to get rid of reliance on pre-trained classifiers?

Using classifier-free guidance to address the same issue as presented in the paper could be an interesting direction for future research. However, classifier-free guidance does not address the issue of explainability, when the classifier latches onto spurious correlations. A pre-trained classifier has a number of other benefits in our field. In contexts where there is a relatively small dataset, training a robust detector would be challenging. Hence, a pre-trained detector trained on a different larger dataset can be used in our proposed framework in order to offset spurious correlations. 

>The technical contribution/novelty of this paper is not clear compared to diffsuion works (e.g. Ho et al.). The benefit of "the flexibility of our method allows it to be used with any pre-trained detector” is actually from diffusion models. This paper is more like an application of classifier-guided diffusion sampling, not an improvement.

We do not claim that our proposed method presents the first classifier-guidance diffusion method nor that it presents an improvement over the original method in general. We show the benefit of _adapting_ a classifier-guidance diffusion strategy in real-world, important image analysis contexts. Our proposed method can overcome the effect of variable artifacts in medical images, which classifiers can latch onto when making decisions, thereby providing more informative and relevant (disease-related) counterfactual explanations.
We would like to thank all the reviewers for their valuable insights and thoughtful review of our paper. We appreciate the thoroughness with which each reviewer reviewed our manuscript, offering insightful suggestions and highlighting areas of improvement. 

We are happy that the reviewers found our paper well-written and well-organized.  We are encouraged with the general consensus that this work presents a successful adaptation of diffusion models in the context of addressing biases in classifier predictions. We have made our code public, for ease of implementation and reproducibility of our approach. We documented our code and it should now be easy to understand and to use.

We have responded to each reviewer’s comments individually below and would be happy to address any comments or concerns.  We addressed each of the concerns and made the associated changes in the original manuscript. This includes the addition of ablation experiments, as requested, which we placed in the appendix. Detailed changes are described below.  We thank the reviewers for their suggestions as we feel they helped improve the paper.
Thank you for your valuable feedback. 

Based on the reviewer's suggestions, we added a set of additional ablations to the paper related to the number of synthesized samples we used for augmentation (Appendix C). The purpose of the experiments was to examine whether additional augmentations provided to the minority subgroups would improve their classification accuracies. In the original paper, we performed augmentation experiments where we illustrated that the addition of 200 synthesized counterfactual images improved the classification performance for the minority subgroups. We now perform experiments where we increased the augmentation by 400 synthesized images for the dot dataset and 1600 synthesized images for the device dataset. A greater augmentation of the original dataset (90% minority and 10% majority) showed significant improvements in the performance of the minority subgroups. We show the additional table (Appendix C) here: 

**Dot Dataset**
|                                        |        **Pleural Effusion**         |                             |       **Healthy**        |                      |
|--------------------------------------------|:-----------------------------------:|:---------------------------:|:------------------------:|:--------------------:|
|                                        |              **Dot**                |          **No Dot**         |          **Dot**         |      **No Dot**      |
||||||
| **ERM augmented with DeCoDEx CFs [200]**      |              90.6                   |            12.1             |          53.1            |          98.0        |
| **ERM augmented with DeCoDEx CFs [400]**      |       **93.8**      |       **26.5**          |       **61.2**          |      **98.3**      |
||||||
| **Group-DRO augmented with DeCoDEx CFs [200]** |              91.0                   |            70.2             |          60.9            |          80.3        |
| **Group-DRO augmented with DeCoDEx CFs [400]** |       **93.1**     |        **81.6**          |       **65.3**          |      **85.9**      |


**Device Dataset**
|                                         |       **Pleural Effusion**        |                            |       **Healthy**        |                    |
|-------------------------------------------------|:--------------------------------------:|:-------------------------:|:----------------:|:------------------------:|
|                                         |         **Support Device**        |    **No Support Device**   |    **Support Device**    | **No Support Device** |
||||||
| **ERM augmented with DeCoDEx CFs [600]**      |              92.6                  |            76.3            |          85.9            |          86.8      |
| **ERM augmented with DeCoDEx CFs [1600]**     |       **92.8**     |        **76.8**         |       **86.8**          |      82.3      |
||||||
| **Group-DRO augmented with DeCoDEx CFs [600]** |              93.2                  |            84.7            |          79.0            |          88.4      |
| **Group-DRO augmented with DeCoDEx CFs [1600]**|       **93.5**     |        **86.5**         |       **79.9**          |      **90.1**    |
Thank you for the detailed review and positive feedback.

Thank you for your minor suggestions which we implemented. We moved the example figure to the front of the paper. We omitted the word ‘established’ for a recently published metric. We made our entire codebase public and documented the complete installation process.

With reference to your question, while the detector is indeed a classifier (indicating the global presence or absence of artifacts), we named it a “detector” in order to avoid confusion with the disease “classifier” in the model. We also wish to clarify that our model is capable of handling different artifacts in factual images. We clarified this in the paper to make sure that this is evident (Section 3.1, page 6): 

“The detector is trained with binary target labels indicating the presence/absence of support devices.  DeCoDEx is capable of handling images with multiple artifacts. These can vary in type, shape, size, location and intensities.”