@misc {43257,
	title = {Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis},
	year = {2023},
	address = {Nordic AI Meet 2023},
	abstract = {Diabetic retinopathy (DR) is a common complication of diabetes that damages the eye and potentially leads to blindness. The severity and treatment choice of DR depends on the presence of medical findings in fundus images.\&nbsp;Much work has been done in developing complex machine learning (ML) models to automatically diagnose DR from fundus images. However, their high level of complexity increases the demand for techniques improving human understanding of the ML models.\&nbsp;Explainable artificial intelligence (XAI) methods can detect weaknesses in ML models and increase trust among end users. In the medical field, it is crucial to explain ML models in order to apply them in the clinic.\&nbsp;While a plethora of XAI methods exists, heatmaps are typically applied for explaining ML models for DR diagnosis. Heatmaps highlight image areas that are regarded as important for the model when making a prediction.\&nbsp;Even though heatmaps are popular, they can be less appropriate in the medical field. Testing with Concept Activation Vectors (TCAV), providing explanations based on human-friendly concepts, can be a more suitable alternative for explaining models for DR diagnosis, but it has not been thoroughly investigated for DR models.\&nbsp;We develop a deep neural network for diagnosing DR from fundus images and apply TCAV for explaining the resulting model. Concept generation with and without masking is compared. Based on diagnostic criteria for DR, we evaluate the model{\textquoteright}s concept ranking for different severity levels of DR.\&nbsp;TCAV can explain individual images to gain insight into a specific case, or an entire class to evaluate overall consistency with diagnostic standards. The most important concepts for the DR model agree with diagnostic criteria for DR. No large differences are detected between the two concept generation approaches.\&nbsp;TCAV is a flexible explanation method where human-friendly concepts provide insights and trust in ML models for medical image analyses, and it shows promising results for DR grading.},
	keywords = {concept-based explanations, diabetic retinopathy, Explainable artificial intelligence},
	author = {Stor{\r a}s, Andrea and Sundgaard, Josefine}
}
@inproceedings {43256,
	title = {Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {Meibomian gland dysfunction is the most common cause of dry eye disease, which is a prevalent condition that can damage the ocular surface and cause reduced vision and substantial pain. Meibum secreted from the meibomian glands makes up the majority of the outer, protective lipid layer of the tear film. Changes in the secreted meibum and markers of glandular damage can be detected through tear sampling.Several studies have investigated the tear film protein expression in meibomian gland dysfunction, but less work apply machine learning to analyze the protein patterns.\&nbsp;We use machine learning and methods from explainable artificial intelligence to detect potential clinically relevant proteins in meibomian gland dysfunction. Two different explainable artificial intelligence methods are compared. Several of the proteins found important in the models have been linked to dry eye disease in the past, while some are novel. Consequently, explainable artificial intelligence methods serve as a promising tool for screening for proteins that are relevant for meibomian gland dysfunction. By doing so, one may be able to discover new biomarkers and treatments, and gain a better understanding of how diseases develop.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics},
	author = {Stor{\r a}s, Andrea and Magn{\o}, Morten and Fineide, Fredrik and Thiede, Bernd and Chen, Xiangjun and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@inproceedings {43340,
	title = {Looking into Concept Explanation Methods for Diabetic Retinopathy Classification },
	journal = {Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2023},
	year = {2023},
	abstract = {Diabetic retinopathy is a common complication of diabetes, and monitoring the progression of retinal abnormalities using fundus imaging is crucial. Because the images must be interpreted by a medical expert, it is infeasible to screen all individuals with diabetes for diabetic retinopathy. Deep learning has shown impressive results for automatic analysis and grading of fundus images. One drawback is, however, the lack of interpretability, which hampers the implementation of such systems in the clinic. Explainable artificial intelligence methods can be applied to explain the deep neural networks. Explanations based on concepts have shown to be intuitive for humans to understand, but have not yet been explored in detail for diabetic retinopathy grading. This work investigates and compares two concept-based explanation techniques for explaining deep neural networks developed for automatic diagnosis of diabetic retinopathy: Quantitative Testing with Concept Activation Vectors and Concept Bottleneck Models. We found that both methods have strengths and weaknesses, and choice of method should take the available data and the end user{\textquoteright}s preferences into account.},
	keywords = {concept-based explanations, diabetic retinopathy, Explainable artificial intelligence},
	author = {Stor{\r a}s, Andrea and Sundgaard, Josefine}
}
@inproceedings {43319,
	title = {Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence},
	journal = {IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023)},
	year = {2023},
	abstract = {Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are commonly visualized through meibography, a technique requiring specialist equipment and knowledge that might not be available to the physician. In the present project we use machine learning on clinical tabular data to predict the degree of meibomian gland dropout. Moreover, we employ explainable artificial intelligence on the best performing algorithms for feature importance evaluation. The best performing algorithms were AdaBoost, multilayer perceptron\&nbsp;and LightGBM\&nbsp;which outperformed the majority vote baseline classifier in every included evaluation metric for both multioutput and binary classification. Through explainable artificial intelligence known associations are validated and novel connections identified and discussed.},
	keywords = {Dry eye disease, Explainable artificial intelligence, Machine learning, meibography, meibomian gland dysfunction},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Riegler, Michael and Utheim, Tor}
}
@article {43321,
	title = {Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis},
	journal = {Diagnostics},
	volume = {13},
	number = {2345},
	year = {2023},
	month = {07/2023},
	publisher = {MDPI},
	abstract = {Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of {\textquotedblleft}black box{\textquotedblright} models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.},
	keywords = {electrocardiograms, Explainable artificial intelligence, heat maps},
	doi = {https://doi.org/10.3390/diagnostics13142345},
	author = {Stor{\r a}s, Andrea and Ole Emil {Andersen} and Lockhart, Sam and Thielemann, Roman and Gnesin, Filip and Thambawita, Vajira and Hicks, Steven and Kanters, J{\o}rgen and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Riegler, Michael}
}
@misc {42696,
	title = {Explainable Artificial Intelligence in Medicine},
	howpublished = {Nordic AI Meet 2022},
	year = {2022},
	abstract = {Machine learning (ML) has shown outstanding abilities to solve a large variety of tasks such as image recognition and natural language processing, which has huge relevance for the medical field. Complex ML models, including convolutional neural networks (CNNs), are used to analyse high dimensional data such as images and videos from medical examinations.\&nbsp;With increasing model complexity, the demand for techniques improving human understanding of the ML models also increases. If medical doctors do not understand how the models work, they might not know when the models are actually wrong or even refuse to use them. This can hamper the implementation of ML systems in the clinic and negatively affect patients.\&nbsp;To promote successful integration of ML systems in the clinic, it is important to provide explanations that establish trust in the models among healthcare personnel.\&nbsp;Explainable artificial intelligence (XAI) aims to provide explanations about ML models and their predictions. Several techniques have already been developed.\&nbsp;Existing XAI methods often fail to meet the requirements of medical doctors, probably because they are not sufficiently involved in the development of the methods.\&nbsp;We develop ML models solving tasks in various medical domains. The resulting models are explained using a selection of existing XAI methods, and the explanations are evaluated by medical experts. Their feedback is used to develop improved XAI methods.\&nbsp;We have investigated established techniques for making ML systems more transparent in the fields of gastroenterology, assisted reproductive technology, organ transplantation and cardiology.\&nbsp;Experiences from our projects will be used to develop new explanation techniques for clinical practice in close collaboration with medical experts.},
	keywords = {Explainable artificial intelligence, Machine learning, medicine},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {28323,
	title = {Unsupervised Image Segmentation via Self-Supervised Learning Image Classification},
	journal = {MediaEval 2021},
	year = {2022},
	publisher = {CEUR Workshop Proceedings},
	edition = {Working Notes Proceedings of the MediaEval 2021 Workshop},
	abstract = {This paper presents the submission of team Medical-XAI for the Medico: Transparency in Medical Image Segmentation task held at MediaEval 2021. We propose an unsupervised method that utilizes tools from the field of explainable artificial intelligence to create segmentation masks. We extract heat maps, which are useful in order to explain how the {\textquoteleft}black box{\textquoteright} model predicts the category of a certain image, and the segmentation masks are directly derived from the heat maps. Our results show that the created masks can capture the relevant findings to a certain extent using only a small amount of image-level labeled data for the classification model and no segmentation masks at all for the training. This is promising for addressing different challenges within the intersection of artificial intelligence for medicine such as availability of data, cost of labeling and interpretable and explainable results.\&nbsp;},
	keywords = {clustering, Explainable artificial intelligence, Global Features, Grad-CAM, Image segmentation, Medical imaging, Polyp Detection, Self-supervised learning},
	url = {http://ceur-ws.org/Vol-3181/},
	author = {Stor{\r a}s, Andrea}
}
