Bio

I'm a Postdoctoral Scholar at Stanford University in the Computational Neuroimage Science Laboratory (CNS Lab) with Prof. Kilian M. Pohl. My research focuses on 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. 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)

Professional Education

  • 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)

Stanford Advisors

Contact

  • Academic
    paschali@stanford.edu
    University - Scholar Department: Psychiatry and Behavioral Sciences Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • Detecting negative valence symptoms in adolescents based on longitudinal self-reports and behavioral assessments. Journal of affective disorders Paschali, M., Kiss, O., Zhao, Q., Adeli, E., Podhajsky, S., Muller-Oehring, E. M., Gotlib, I. H., Pohl, K. M., Baker, F. C. 2022

    Abstract

    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

  • Bridging the Gap Between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing Paschali, M., Zhao, Q., Adeli, E., Pohl, K. M., Rekik, Adeli, E., Park, S. H., Cintas, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 13-23

    View details for DOI 10.1007/978-3-031-16919-9_2

    View details for Web of Science ID 000867616800002

  • OperA: Attention-Regularized Transformers for Surgical Phase Recognition Czempiel, T., Paschali, M., Ostler, D., Kim, S., Busam, B., Navab, N., DeBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 604-614

    View details for DOI 10.1007/978-3-030-87202-1_58

    View details for Web of Science ID 000712021400058

  • Rethinking Ultrasound Augmentation: A Physics-Inspired Approach Tirindelli, M., Eilers, C., Simson, W., Paschali, M., Azampour, M., Navab, N., DeBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 690-700

    View details for DOI 10.1007/978-3-030-87237-3_66

    View details for Web of Science ID 000712019200066

  • Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs Kim, S., Goli, L., Paschali, M., Khakzar, A., Keicher, M., Czempiel, T., Burian, E., Braren, R., Navab, N., Wendler, T., DeBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 273-282

    View details for DOI 10.1007/978-3-030-87234-2_26

    View details for Web of Science ID 000712024400026

  • SIGNAL CLUSTERING WITH CLASS-INDEPENDENT SEGMENTATION Gasperini, S., Paschali, M., Hopke, C., Wittmann, D., Navab, N., IEEE IEEE. 2020: 3982-3986

    View details for Web of Science ID 000615970404046

  • Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning Hase, H., Azampour, M., Tirindelli, M., Paschali, M., Simson, W., Fatemizadeh, E., Navab, N., IEEE IEEE. 2020: 5534-5541

    View details for DOI 10.1109/IROS45743.2020.9340913

    View details for Web of Science ID 000714033803042

  • Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness Paschali, M., Simson, W., Roy, A., Goebl, R., Wachinger, C., Navab, N., Chung, A. C., Gee, J. C., Yushkevich, P. A., Bao, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 517-529

    View details for DOI 10.1007/978-3-030-20351-1_40

    View details for Web of Science ID 000493380900040

  • 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation Paschali, M., Gasperini, S., Roy, A., Fang, M., Navab, N., Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P. T., Khan, A. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 438-446

    View details for DOI 10.1007/978-3-030-32248-9_49

    View details for Web of Science ID 000548733600049

  • Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples Paschali, M., Conjeti, S., Navarro, F., Navab, N., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 493-501

    View details for DOI 10.1007/978-3-030-00928-1_56

    View details for Web of Science ID 000477770600056

https://orcid.org/0000-0003-4967-3137