I am a Postdoctoral Scholar working with Prof. Akshay Chaudhari and Prof. David Larson in the Department of Radiology at Stanford focusing on evaluating the robustness of large-scale AI models and identifying early disease biomarkers.
Until August 2023, I was a Postdoctoral Scholar in the Computational Neuroimage Science Laboratory (CNS Lab) with Prof. Kilian M. Pohl working on multi-modal machine learning models that can improve the understanding, diagnosis, and treatment of neuropsychiatric disorders.
Previously I completed my PhD at the Chair for Computer Aided Medical Procedures at the Technical University of Munich under the supervision of Prof. Nassir Navab and my dissertation was titled "Learning Robust Representations for Medical Diagnosis". I am passionate about designing trustworthy deep learning methods for challenging applications.
Honors & Awards
Best Paper Award, PRedictive Intelligence In MEdicine - PRIME - MICCAI (September 2022)
Best Paper Award, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging - UNSURE - MICCAI (September 2021)
Graduate Student Travel Award, Medical Image Computing and Computer Assisted Interventions (MICCAI) (October 2019)
Best Poster Award, International Conference on Information Processing in Medical Imaging (IPMI) (June 2019)
Boards, Advisory Committees, Professional Organizations
Public Relations Officer, MICCAI Student Board (2017 - 2020)
PhD, Technical University of Munich, Learning Robust Representations for Medical Diagnosis (2021)
M.Sc., Technical University of Munich, Informatics (2017)
B.Sc., Aristotle University of Thessaloniki, Informatics (2015)
Current Research and Scholarly Interests
My research focuses on utilizing machine learning models to enhance the understanding, diagnosis, and treatment of clinical disorders. I am interested in multi-modal learning, combining imaging data like MRI and CT scans with non-imaging data such as electronic health records, creating more holistic and accurate diagnostic models. I am also interested in the robustness of deep neural networks under domain shifts, investigating how models perform when faced with changes in input data distributions.
Finally, I am interested in early biomarker identification using AI model interpretability, to enable the early detection and targeted treatment of chronic disorders.
Multimodal graph attention network for COVID-19 outcome prediction.
2023; 13 (1): 19539
When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients can often only be determined on short notice by acute indicators such as vital signs (e.g., breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic, multimodal graph-based approach combining imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph that shows a clustering of patients. For each patient in the graph, we extract radiomic features from a segmentation network that also serves as a latent image feature encoder. Together with clinical patient data like vital signs, demographics, and lab results, these modalities are combined into a multimodal representation of each patient. This feature extraction is trained end-to-end with an image-based Graph Attention Network to process the population graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation, and mortality. To combine multiple modalities, radiomic features are extracted from chest CTs using a segmentation neural network. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany and the publicly available iCTCF dataset show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention increases understanding of the patient relationships within the population graph and provides insight into the network's decision-making process.
View details for DOI 10.1038/s41598-023-46625-8
View details for PubMedID 37945590
View details for PubMedCentralID 7869614
Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning.
2023; 137: 107179
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data.
View details for DOI 10.1016/j.ultras.2023.107179
View details for PubMedID 37939413
- Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT Scans IEEE ACCESS 2023; 11: 77596-77607
Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing.
PRedictive Intelligence in MEdicine. PRIME (Workshop)
2022; 13564: 13-23
A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative valence, a symptom of major depressive disorder according to the NIMH Research Domain Criteria (RDoC). Our method successfully identifies categories of risk factors that further explain the symptom.
View details for DOI 10.1007/978-3-031-16919-9_2
View details for PubMedID 36342897
View details for PubMedCentralID PMC9632755
Detecting negative valence symptoms in adolescents based on longitudinal self-reports and behavioral assessments.
Journal of affective disorders
BACKGROUND: Given the high prevalence of depressive symptoms reported by adolescents and associated risk of experiencing psychiatric disorders as adults, differentiating the trajectories of the symptoms related to negative valence at an individual level could be crucial in gaining a better understanding of their effects later in life.METHODS: A longitudinal deep learning framework is presented, identifying self-reported and behavioral measurements that detect the depressive symptoms associated with the Negative Valence System domain of the NIMH Research Domain Criteria (RDoC).RESULTS: Applied to the annual records of 621 participants (age range: 12 to 17 years) of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), the deep learning framework identifies predictors of negative valence symptoms, which include lower extraversion, poorer sleep quality, impaired executive control function and factors related to substance use.LIMITATIONS: The results rely mainly on self-reported measures and do not provide information about the underlying neural correlates. Also, a larger sample is required to understand the role of sex and other demographics related to the risk of experiencing symptoms of negative valence.CONCLUSIONS: These results provide new information about predictors of negative valence symptoms in individuals during adolescence that could be critical in understanding the development of depression and identifying targets for intervention. Importantly, findings can inform preventive and treatment approaches for depression in adolescents, focusing on a unique predictor set of modifiable modulators to include factors such as sleep hygiene training, cognitive-emotional therapy enhancing coping and controllability experience and/or substance use interventions.
View details for DOI 10.1016/j.jad.2022.06.002
View details for PubMedID 35688394
- OperA: Attention-Regularized Transformers for Surgical Phase Recognition SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 604-614
- Rethinking Ultrasound Augmentation: A Physics-Inspired Approach SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 690-700
- Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 273-282
- Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning IEEE. 2020: 5534-5541
SIGNAL CLUSTERING WITH CLASS-INDEPENDENT SEGMENTATION
IEEE. 2020: 3982-3986
View details for Web of Science ID 000615970404046
- Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 517-529
- 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 438-446
- Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 493-501