Publications
Poster
Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis
Nordic AI Meet 2023, 2023.Status: Accepted
Concept Explanations for Deep Learning-Based Diabetic Retinopathy Diagnosis
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. 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. 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. 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. 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. 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’s concept ranking for different severity levels of DR. 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. 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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2023 |
Place Published | Nordic AI Meet 2023 |
Keywords | concept-based explanations, diabetic retinopathy, Explainable artificial intelligence |
Proceedings, refereed
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
In IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023), 2023.Status: Accepted
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence
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. 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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2023) |
Keywords | Dry eye disease, Explainable artificial intelligence, Machine learning, meibomian gland dysfunction, proteomics |
Multimedia datasets: challenges and future possibilities
In International conference on multimedia modeling, 2023.Status: Accepted
Multimedia datasets: challenges and future possibilities
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | International conference on multimedia modeling |
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
In ARVO Annual Meeting, 2023.Status: Accepted
Using explainable artificial intelligence (XAI) to explore factors affecting meibomian gland (MG) dropout
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | ARVO Annual Meeting |
Journal Article
Artificial intelligence in dry eye disease
The Ocular Surface 23 (2022): 74-86.Status: Published
Artificial intelligence in dry eye disease
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 ‘AI’ 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 interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | The Ocular Surface |
Volume | 23 |
Pagination | 74 - 86 |
Date Published | Jan-01-2022 |
Publisher | Elsevier |
ISSN | 15420124 |
Keywords | artificial intelligence, Dry eye disease, Machine learning |
URL | https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324 |
DOI | 10.1016/j.jtos.2021.11.004 |
Automatic Tracking of the ICSI procedure using Deep Learning
Human Reproduction 37, no. Supplement_1 (2022).Status: Published
Automatic Tracking of the ICSI procedure using Deep Learning
Study question
Can deep learning be used to detect and track spermatozoa and the different parts of an ICSI procedure?
Summary answer
Deep learning can be used as a tool to assist and organize the contents of an ICSI procedure.
What is known already
Sperm 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 “best” 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, duration
The 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, methods
The 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 µl 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 chance
Mean 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 caution
The 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 findings
Deep 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 number
not applicable
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Human Reproduction |
Volume | 37 |
Issue | Supplement_1 |
Date Published | 07/2022 |
Publisher | Oxford University Press |
ISSN | 0268-1161 |
URL | https://academic.oup.com/humrep/article/37/Supplement_1/deac107.261/6619904 |
DOI | 10.1093/humrep/deac107.261 |
Bias og kvantitativ analyse innen velferd
Tidsskrift for velferdsforskning 25, no. 3 (2022): 1-24.Status: Published
Bias og kvantitativ analyse innen velferd
Ifølge Norges nasjonale strategi for kunstig intelligens (2020) er offentlig forvaltning og helse blant Norges satsningsområder for bruk av kunstig intelligens. Maskinlæring er en undergruppe av kunstig intelligens med potensiale for å løse en rekke utfordringer, men som også gir opphav til utfordringer. En slik utfordring er bias, eller skjevhet. Et eksempel på skjevhet er at tilstedeværende ulikheter i samfunnet representeres i datagrunnlaget maskinlæringsmodeller utvikles på. De resulterende modellene står dermed i fare for å adoptere og videreføre disse ulikhetene. En utfordring er at skjevhet har ulike definisjoner innen ulike fagområder, og kan ha mange ulike opphav. Vi bidrar til å løse denne utfordringen ved å gi en oversikt over ulike typer skjevhet og deres opphav med illustrasjoner fra et velferdsperspektiv, samt avklarer forskjellen til det nærliggende konseptet rettferdighet. Vi demonstrerer utfordringer relatert til databaserte modellers oppførsel ved å benytte maskinlæring til å predikere fremtidig ressursbehov i helsevesenet, spesifikt antall legebesøk i kommuner. Vi demonstrerer ulike typer skjevheter, diskuterer mulige løsninger og bruker metoder fra forklarbar kunstig intelligens for å analysere opphavet til skjevheter i forklaringsvariablene. Det finnes ingen universell løsning for å håndtere alle typer skjevheter, men skjevhet må tas høyde for i alle deler av en kvantitativ analyse.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Tidsskrift for velferdsforskning |
Volume | 25 |
Issue | 3 |
Pagination | 1-24 |
Publisher | Universitetsforlaget |
Place Published | Tidsskrift for velferdsforskning |
Keywords | forklarbar kunstig intelligens, Maskinlæring, skjevhetsbegreper, velferdsforskning, XGBoost |
DOI | 10.18261/tfv.25.3.3 |
Predicting an unstable tear film through artificial intelligence
Scientific Reports 12, no. 1 (2022): 21416.Status: Published
Predicting an unstable tear film through artificial intelligence
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 ZeroR 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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Scientific Reports |
Volume | 12 |
Issue | 1 |
Pagination | 21416 |
Date Published | 12/2022 |
Publisher | Springer Nature |
URL | https://www.nature.com/articles/s41598-022-25821-y |
DOI | 10.1038/s41598-022-25821-y |
Real-time deep learning based multi object tracking of spermatozoa in fresh samples
Human Reproduction 37, no. Supplement_1, July 2022, deac107.104 (2022).Status: Published
Real-time deep learning based multi object tracking of spermatozoa in fresh samples
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Human Reproduction |
Volume | 37 |
Issue | Supplement_1, July 2022, deac107.104 |
Date Published | 06/2022 |
Publisher | Oxford University Press |
DOI | 10.1093/humrep/deac107.104 |
Proceedings, refereed
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
In NAIS: Symposium of the Norwegian AI Society . Vol. 1650. NAIS 2022, 2022.Status: Published
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
The in vitro 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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | NAIS: Symposium of the Norwegian AI Society |
Volume | 1650 |
Pagination | 1-11 |
Publisher | NAIS 2022 |
ISBN Number | 978-3-031-17029-4 |
Keywords | clustering, Computer Vision and Pattern Recognition (cs.CV), human reproduction, medical videos, Unsupervised learning |
DOI | 10.1007/978-3-031-17030-0_9 |
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework
In 2022 IEEE International Symposium on Multimedia (ISM). Italy: IEEE, 2022.Status: Published
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework
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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 2022 IEEE International Symposium on Multimedia (ISM) |
Pagination | 161-162 |
Publisher | IEEE |
Place Published | Italy |
URL | https://ieeexplore.ieee.org/document/10019678 |
DOI | 10.1109/ISM55400.2022.00035 |
Explainability methods for machine learning systems for multimodal medical datasets: research proposal
In ACM Multimedia Systems (MMSys) Conference. ACM, 2022.Status: Published
Explainability methods for machine learning systems for multimodal medical datasets: research proposal
This paper contains the research proposal of Andrea M. Storås 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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | ACM Multimedia Systems (MMSys) Conference |
Pagination | 347-351 |
Publisher | ACM |
ISBN Number | 978-1-4503-9283-9/22/06 |
DOI | 10.1145/3524273.3533925 |
Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets
In Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22). ACM, 2022.Status: Published
Huldra: A Framework for Collecting Crowdsourced Feedback on Multimedia Assets
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22) |
Pagination | 203-209 |
Publisher | ACM |
DOI | 10.1145/3524273.3532887 |
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
In IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2022.Status: Published
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) |
Pagination | 66-71 |
Date Published | 07/2022 |
Publisher | IEEE |
DOI | 10.1109/CBMS55023.2022.00019 |
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning
In 35th IEEE CBMS International Symposium on Computer-Based Medical Systems. IEEE, 2022.Status: Published
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning
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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | 35th IEEE CBMS International Symposium on Computer-Based Medical Systems |
Pagination | 38-43 |
Publisher | IEEE |
Keywords | Machine learning, personalized medicine, transplantation |
DOI | 10.1109/CBMS55023.2022.00014 |
Unsupervised Image Segmentation via Self-Supervised Learning Image Classification
In MediaEval 2021. Working Notes Proceedings of the MediaEval 2021 Workshop ed. CEUR Workshop Proceedings, 2022.Status: Published
Unsupervised Image Segmentation via Self-Supervised Learning Image Classification
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 `black box' 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.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | MediaEval 2021 |
Edition | Working Notes Proceedings of the MediaEval 2021 Workshop |
Publisher | CEUR Workshop Proceedings |
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/ |
Talks, invited
Explainable Artificial Intelligence in Medicine
In Nordic AI Meet 2022, 2022.Status: Accepted
Explainable Artificial Intelligence in Medicine
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. 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. 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. Explainable artificial intelligence (XAI) aims to provide explanations about ML models and their predictions. Several techniques have already been developed. 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. 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. We have investigated established techniques for making ML systems more transparent in the fields of gastroenterology, assisted reproductive technology, organ transplantation and cardiology. Experiences from our projects will be used to develop new explanation techniques for clinical practice in close collaboration with medical experts.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Nordic AI Meet 2022 |
Keywords | Explainable artificial intelligence, Machine learning, medicine |
Poster
Predicting drug exposure in kidney transplanted patients using machine learning
NORA Annual Conference, Stavanger, Norway, 2022.Status: Published
Predicting drug exposure in kidney transplanted patients using machine learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Poster |
Year of Publication | 2022 |
Place Published | NORA Annual Conference, Stavanger, Norway |
Type of Work | Poster presentation |
Miscellaneous
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
arXiv, 2022.Status: Published
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Miscellaneous |
Year of Publication | 2022 |
Publisher | arXiv |
URL | https://arxiv.org/abs/2204.00617 |
DOI | 10.48550/arXiv.2204.00617 |