@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 {43282,
	title = {Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks},
	journal = {International conference on multimedia modeling},
	year = {2023},
	publisher = {Springer International Publishing},
	abstract = {High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs{\textquoteright} potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients{\textquoteright} privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.},
	keywords = {deep learning, Medical data, synthetic data generation},
	doi = {10.1007/978-3-031-27077-2_34},
	author = {Alireza Hossein Za {Nik} and Riegler, Michael and Halvorsen, P{\r a}l and Stor{\r a}s, Andrea}
}
@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 {43077,
	title = {Multimedia datasets: challenges and future possibilities},
	journal = {International conference on multimedia modeling},
	year = {2023},
	doi = {https://doi.org/10.1007/978-3-031-27818-1_58},
	author = {Nguyen, Thu and Stor{\r a}s, Andrea and Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Riegler, Michael}
}
@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}
}
@inproceedings {43320,
	title = {Predicting the degree of meibomian gland dropout with artificial intelligence},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Magn{\o}, Morten and Hugo Lewi {Hammer} and Chen, Xiangjun and Khan, Ayyad and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@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}
}
@inproceedings {43255,
	title = {Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout},
	journal = {ARVO Annual Meeting},
	year = {2023},
	author = {Stor{\r a}s, Andrea and Fineide, Fredrik and Hugo Lewi {Hammer} and Khan, Ayyad and Magn{\o}, Morten and Xiangjung, Chen and Halvorsen, P{\r a}l and Utheim, Tor and Riegler, Michael}
}
@article {28224,
	title = {Artificial intelligence in dry eye disease},
	journal = {The Ocular Surface},
	volume = {23},
	year = {2022},
	month = {Jan-01-2022},
	pages = {74 - 86},
	publisher = {Elsevier},
	abstract = {Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term {\textquoteleft}AI{\textquoteright} is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of\&nbsp;interferometry, slit-lamp and\&nbsp;meibography\&nbsp;images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.},
	keywords = {artificial intelligence, Dry eye disease, Machine learning},
	issn = {15420124},
	doi = {10.1016/j.jtos.2021.11.004},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Grauslund, Jakob and Hugo Lewi {Hammer} and Yazidi, Anis and Halvorsen, P{\r a}l and Kjell G. {Gundersen} and Tor P. {Utheim} and Catherine J. {Jackson}}
}
@article {42723,
	title = {Automatic Tracking of the ICSI procedure using Deep Learning},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	month = {07/2022},
	publisher = {Oxford University Press},
	abstract = {Study questionCan deep learning be used to detect and track spermatozoa and the different parts of an ICSI procedure?Summary answerDeep learning can be used as a tool to assist and organize the contents of an ICSI procedure.What is known alreadySperm tracking has been a topic of research and practice for many years, especially in the context of computer-aided sperm analysis (CASA). Recent studies have proposed using deep learning algorithms to track spermatozoa for spermatozoon selection in human and animal samples. One critical part of performing ICSI involves the selection of the {\textquotedblleft}best{\textquotedblright} spermatozoon for injection, but other parts of the procedure may also be of importance. However, as far as we know, tracking using deep learning has not been applied to the ICSI procedure, where detecting instruments and the oocyte could also be helpful in post-analysis and training.Study design, size, durationThe study was performed using three anonymized videos of the ICSI procedure. The frames of the videos were manually annotated by data scientists and verified by an embryologist. The annotations were bounding boxes around specific parts of the ICSI procedure, including sperm, pipettes, and the oocyte. We trained a YOLOv5 model on the collected data, where two videos were used for training and one video for validation.Participants/materials, setting, methodsThe videos of the ICSI procedure were captured at 200x magnification with a DeltaPix camera at Fertilitetssenteret in Oslo, Norway. ICSI was performed using a Nikon ECLIPSE TE2000-S microscope connected with Eppendorf TransferMan 4m micromanipulators. The spermatozoa were immobilised in 5 {\textmu}l Polyvinylpyrrolidone (PVP; CooperSurgical). The videos had a resolution of 1920x1080 and were resized to 640x640 before being processed by the YOLOv5 model. The data will be made public in a later study.Main results and the role of chanceMean average precision (mAP) with the threshold of 0.5 (mAP@.5) is the main quantitative parameter measured in the YOLOv5 model. All the experiments were performed using three-fold cross-validation, where we present the average metrics calculated over the three folds. Overall, the method showed an average mAP@.5 of 0.50 across all predicted classes, which means that the method can track the different components with good accuracy. Looking closer at the individual classes, we see that instruments like the holding pipette and ICSI pipette are detected with high accuracy with a mAP@.5 of 0.87 and 0.94, respectively. The oocyte is also easily tracked with a mAP@.5 of 0.92. The first polar body is well detected with a mAP@.5 of 0.65. The model has issues detecting and tracking individual sperm (both outside and within the pipette), where the method achieved a mAP@.5 of 0.46 for tracking sperm outside the pipette and 0.03 for the sperm inside the pipette. The low score of detecting the sperm in the pipette can be explained by the often unclear visibility of the sperm through the pipette and the low number of training samples.Limitations, reasons for cautionThe limited sample size makes the generalizability of the method difficult to determine. A more extensive evaluation is necessary. Moreover, as the currency study focuses on tracking, patient information and clinical outcome were not included in the analysis.Wider implications of the findingsDeep learning has the potential to aid embryologists to perform successful ICSI through tracking and detection of spermatozoa, pipettes, and the oocyte. This could potentially lead to better internal quality control and teaching possibilities, and hopefully better results.Trial registration numbernot applicable},
	issn = {0268-1161},
	doi = {10.1093/humrep/deac107.261},
	url = {https://academic.oup.com/humrep/article/37/Supplement_1/deac107.261/6619904},
	author = {Hicks, Steven and Thambawita, Vajira and Stor{\r a}s, Andrea and Trine B. {Haugen} and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael and Mette Haug {Stensen}}
}
@inproceedings {42538,
	title = {Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure},
	journal = {NAIS: Symposium of the Norwegian AI Society },
	volume = {1650},
	year = {2022},
	pages = {1-11},
	publisher = {NAIS 2022},
	abstract = {The\&nbsp;in vitro\&nbsp;fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.},
	keywords = {clustering, Computer Vision and Pattern Recognition (cs.CV), human reproduction, medical videos, Unsupervised learning},
	isbn = {978-3-031-17029-4},
	doi = {10.1007/978-3-031-17030-0_9},
	author = {Stor{\r a}s, Andrea and Riegler, Michael and Trine B. {Haugen} and Thambawita, Vajira and Hicks, Steven and Hugo Lewi {Hammer} and Kakulavarapu, Radhika and Halvorsen, P{\r a}l and Stensen, Mette}
}
@article {42541,
	title = {Bias og kvantitativ analyse innen velferd},
	journal = {Tidsskrift for velferdsforskning},
	volume = {25},
	year = {2022},
	pages = {1-24},
	publisher = {Universitetsforlaget},
	address = {Tidsskrift for velferdsforskning},
	abstract = {If{\o}lge Norges nasjonale strategi for kunstig intelligens (2020) er offentlig forvaltning og helse blant Norges satsningsomr{\r a}der for bruk av kunstig intelligens. Maskinl{\ae}ring er en undergruppe av kunstig intelligens med potensiale for {\r a} l{\o}se en rekke utfordringer, men som ogs{\r a} gir opphav til utfordringer. En slik utfordring er bias, eller skjevhet. Et eksempel p{\r a} skjevhet er at tilstedev{\ae}rende ulikheter i samfunnet representeres i datagrunnlaget maskinl{\ae}ringsmodeller utvikles p{\r a}. De resulterende modellene st{\r a}r dermed i fare for {\r a} adoptere og videref{\o}re disse ulikhetene. En utfordring er at skjevhet har ulike definisjoner innen ulike fagomr{\r a}der, og kan ha mange ulike opphav. Vi bidrar til {\r a} l{\o}se denne utfordringen ved {\r a} gi en oversikt over ulike typer skjevhet og deres opphav med illustrasjoner fra et velferdsperspektiv, samt avklarer forskjellen til det n{\ae}rliggende konseptet rettferdighet. Vi demonstrerer utfordringer relatert til databaserte modellers oppf{\o}rsel ved {\r a} benytte maskinl{\ae}ring til {\r a} predikere fremtidig ressursbehov i helsevesenet, spesifikt antall legebes{\o}k i kommuner. Vi demonstrerer ulike typer skjevheter, diskuterer mulige l{\o}sninger og bruker metoder fra forklarbar kunstig intelligens for {\r a} analysere opphavet til skjevheter i forklaringsvariablene. Det finnes ingen universell l{\o}sning for {\r a} h{\r a}ndtere alle typer skjevheter, men skjevhet m{\r a} tas h{\o}yde for i alle deler av en kvantitativ analyse.},
	keywords = {forklarbar kunstig intelligens, Maskinl{\ae}ring, skjevhetsbegreper, velferdsforskning, XGBoost},
	doi = {https://doi.org/10.18261/tfv.25.3.3},
	author = {Stor{\r a}s, Andrea and Prabhu, Robindra and Hugo Lewi {Hammer} and Str{\"u}mke, Inga}
}
@inproceedings {43138,
	title = {Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework},
	journal = {2022 IEEE International Symposium on Multimedia (ISM)},
	year = {2022},
	pages = {161-162},
	publisher = {IEEE},
	address = {Italy},
	abstract = {Subjective user studies are important to ensure the fidelity and usability of systems that generate multimedia content. Testing how end-users and domain experts perceive multimedia assets might provide crucial information. In this paper, we present our experiences with the open source hybrid crowdsourced-remote user survey framework called Huldra, which is intended for conducting web-based subjective user studies and aims to integrate the individual benefits associated with traditional, crowdsourced, and remote methods. We disseminate our experiences and insights from two actively deployed use cases and discuss challenges and opportunities associated with using Huldra as a framework for conducting user studies.},
	doi = {10.1109/ISM55400.2022.00035},
	url = {https://ieeexplore.ieee.org/document/10019678},
	author = {Midoglu, Cise and Stor{\r a}s, Andrea and Saeed Shafiee {Sabet} and Hammou, Malek and Hicks, Steven and Str{\"u}mke, Inga and Riegler, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {42464,
	title = {Explainability methods for machine learning systems for multimodal medical datasets: research proposal},
	journal = {ACM Multimedia Systems (MMSys) Conference},
	year = {2022},
	pages = {347-351},
	publisher = {ACM},
	abstract = {This paper contains the research proposal of Andrea M. Stor{\r a}s that was presented at the MMSys 2022 doctoral symposium. Machine learning models have the ability to solve medical tasks with a high level of performance, e.g., classifying medical videos and detecting anomalies using different sources of data. However, many of these models are highly complex and difficult to understand. Lack of interpretability can limit the use of machine learning systems in the medical domain. Explainable artificial intelligence provides explanations regarding the models and their predictions. In this PhD project, we develop machine learning models for automatic analysis of medical data and explain the results using established techniques from the field of explainable artificial intelligence. Current research indicate that there are still open issues to be solved in order for end users to understand multimedia systems powered by machine learning. Consequently, new explanation techniques will also be developed. Different types of medical data are applied in order to investigate the generalizability of the methods.},
	isbn = {978-1-4503-9283-9/22/06},
	doi = {10.1145/3524273.3533925},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l}
}
@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 {42442,
	title = {Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSys {\textquoteright}22)},
	year = {2022},
	pages = {203-209},
	publisher = {ACM},
	doi = {10.1145/3524273.3532887},
	author = {Hammou, Malek and Midoglu, Cise and Hicks, Steven and Stor{\r a}s, Andrea and Saeed Shafiee {Sabet} and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {42722,
	title = {PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps},
	journal = {IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2022},
	month = {07/2022},
	pages = {66-71},
	publisher = {IEEE},
	abstract = {Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1\% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.},
	doi = {10.1109/CBMS55023.2022.00019},
	author = {Jan Andre {Fageren} and Thambawita, Vajira and Stor{\r a}s, Andrea and Parasa, Sravanthi and de Lange, Thomas and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {43095,
	title = {Predicting an unstable tear film through artificial intelligence},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {12/2022},
	pages = {21416},
	publisher = {Springer Nature},
	abstract = {Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with\&nbsp;ZeroR\&nbsp;according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.},
	doi = {10.1038/s41598-022-25821-y},
	url = {https://www.nature.com/articles/s41598-022-25821-y},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Chen, Xiangjun and Morten S. {Magn{\o}} and Yazidi, Anis and Riegler, Michael and Tor Paaske {Utheim}}
}
@misc {42540,
	title = {Predicting drug exposure in kidney transplanted patients using machine learning},
	year = {2022},
	type = {Poster presentation},
	address = {NORA Annual Conference, Stavanger, Norway},
	author = {Stor{\r a}s, Andrea and Riegler, Michael and Halvorsen, P{\r a}l and {\r A}sberg, Anders and Str{\"u}mke, Inga}
}
@inproceedings {42542,
	title = {Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning},
	journal = {35th IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2022},
	pages = {38-43},
	publisher = { IEEE},
	abstract = {Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.},
	keywords = {Machine learning, personalized medicine, transplantation},
	doi = {10.1109/CBMS55023.2022.00014},
	author = {Stor{\r a}s, Andrea and {\r A}sberg, Anders and Halvorsen, P{\r a}l and Riegler, Michael and Str{\"u}mke, Inga}
}
@article {42720,
	title = {Real-time deep learning based multi object tracking of spermatozoa in fresh samples},
	journal = {Human Reproduction},
	volume = {37},
	year = {2022},
	month = {06/2022},
	publisher = {Oxford University Press},
	doi = {https://doi.org/10.1093/humrep/deac107.104},
	author = {Thambawita, Vajira and Hicks, Steven and Stor{\r a}s, Andrea and Witczak, Oliwia and Jorunn M. {Andersen} and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael and Haugen B. {Trine}}
}
@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}
}
@misc {42764,
	title = {Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations},
	year = {2022},
	publisher = {arXiv},
	doi = {10.48550/arXiv.2204.00617},
	url = {https://arxiv.org/abs/2204.00617},
	author = {Hicks, Steven and Stor{\r a}s, Andrea and Riegler, Michael and Midoglu, Cise and Hammou, Malek and de Lange, Thomas and Parasa, Sravanthi and Halvorsen, P{\r a}l and Str{\"u}mke, Inga}
}
