Ece Özkan Elsen


Last Name

Özkan Elsen

First Name

Ece

ORCID

0000-0002-9889-6348

Organisational unit

Filters

2015 - 2019112020 - 202412
Reset filters

Search Results

Publications 1 - 10 of 23
  • Compliance Boundary Conditions for Simulating Deformations in a Limited Target Region
    Item type: Conference Paper
    Özkan Elsen, Ece; Goksel, Orcun (2015)
    2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • Compliance Boundary Conditions for Elasticity Reconstruction Using FEM Inverse Problem
    Item type: Other Conference Item
    Özkan Elsen, Ece; Goksel, Orcun (2016)
    Proceedings of the Fifteenth International Tissue Elasticity Conference
  • Inverse Problem of Ultrasound Beamforming with Sparsity in Time and Frequency Domain
    Item type: Conference Paper
    Özkan Elsen, Ece; Goksel, Orcun (2016)
    2016 IEEE International Ultrasonics Symposium (IUS)
  • Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms
    Item type: Journal Article
    Ragnarsdottir, Hanna; Özkan Elsen, Ece; Michel, Holger; et al. (2024)
    International Journal of Computer Vision
    Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Thus, accurate and early detection of PH and the classification of its severity is crucial for appropriate and successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. Little effort has been directed towards automatic assessment of PH using echocardiography, and the few proposed methods only focus on binary PH classification on the adult population. In this work, we present an explainable multi-view video-based deep learning approach to predict and classify the severity of PH for a cohort of 270 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation and 0.63 for severity prediction and 0.78 for binary detection on the held-out test set. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.
  • Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms
    Item type: Conference Paper
    Ragnarsdottir, Hanna; Manduchi, Laura; Michel, Holger; et al. (2022)
    Lecture Notes in Computer Science ~ Pattern Recognition
    Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Therefore, accurate and early detection of PH is crucial for successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we present an interpretable multi-view video-based deep learning approach to predict PH for a cohort of 194 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice.
  • Multiview Concept Bottleneck Models Applied to Diagnosing Pediatric Appendicitis
    Item type: Other Conference Item
    Klimiene, Ugne; Marcinkevičs, Ričards; Reis Wolfertstetter, Patricia; et al. (2022)
    Arguably, interpretability is one of the guiding principles behind the development of machine-learning-based healthcare decision support tools and computer-aided diagnosis systems. There has been a renewed interest in interpretable classification based on high-level concepts, including, among other model classes, the re-exploration of concept bottleneck models. By their nature, medical diagnosis, patient management, and monitoring require the assessment of multiple views and modalities to form a holistic representation of the patient's state. For instance, in ultrasound imaging, a region of interest might be registered from multiple views that are informative about different sets of clinically relevant features. Motivated by this, we extend the classical concept bottleneck model to the multiview classification setting by representation fusion across the views. We apply our multiview concept bottleneck model to the dataset of ultrasound images acquired from a cohort of pediatric patients with suspected appendicitis to predict the disease. The results suggest that auxiliary supervision from the concepts and aggregation across multiple views help develop more accurate and interpretable classifiers.
  • Compliance boundary conditions for patient-specific deformation simulation using the finite element method
    Item type: Journal Article
    Özkan Elsen, Ece; Goksel, Orcun (2018)
    Biomedical Physics & Engineering Express
  • Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
    Item type: Working Paper
    Marcinkevičs, Ričards; Özkan Elsen, Ece; Vogt, Julia E. (2022)
    Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.
  • Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis
    Item type: Working Paper
    Marcinkevičs, Ričards; Reis Wolfertstetter, Patricia; Klimiene, Ugne; et al. (2023)
    arXiv
    Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. With recent advances in machine learning, data-driven decision support could help clinicians diagnose and manage patients while reducing the number of non-critical surgeries. Previous decision support systems for appendicitis focused on clinical, laboratory, scoring and computed tomography data, mainly ignoring abdominal ultrasound, a noninvasive and readily available diagnostic modality. To this end, we developed and validated interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Our methodological contribution is the generalization of concept bottleneck models to prediction problems with multiple views and incomplete concept sets. Notably, such models lend themselves to interpretation and interaction via high-level concepts understandable to clinicians without sacrificing performance or requiring time-consuming image annotation when deployed.
Publications 1 - 10 of 23