Zilverling

S. Pathak MSc (Shreyasi)

Promovendus
EEMCS-CS-DMB
Zilverling 4057
s.pathak@utwente.nl
+31534896323
Visitekaartje

Expertises

  • Computer Science

    • Models
    • Explainable Artificial Intelligence
    • Machine Learning
    • Records
    • Survey
    • Channels
    • Deep Learning
    • Generation Model

Organisaties

Publicaties

2024

Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges (2024)[Working paper › Preprint]. Pathak, S., Schlötterer, J., Veltman, J., Geerdink, J., Keulen, M. v. & Seifert, C.

2023

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023)ACM computing surveys, 55(13s). Article 295. Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., Van Keulen, M. & Seifert, C.https://doi.org/10.1145/3583558Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings (2023)[Working paper › Preprint]. ArXiv.org. Pathak, S., Schlötterer, J., Geerdink, J., Vijlbrief, O. D., Keulen, M. v. & Seifert, C.Benchmarking eXplainable AI: A Survey on Available Toolkits and Open Challenges (2023)In Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 (pp. 6665-6673) (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2023-August). International Joint Conferences on Artificial Intelligence. Le, P. Q., Nauta, M., Nguyen, V. B., Pathak, S., Schlötterer, J. & Seifert, C.

2022

Channel Contribution In Deep Learning Based Automatic Sleep Scoring – How Many Channels Do We Need? (2022)IEEE transactions on neural systems and rehabilitation engineering, 31, 494-505. Lu, C., Pathak, S., Englebienne, G. & Seifert, C.https://doi.org/10.1109/TNSRE.2022.3227040From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2022)[Working paper › Preprint]. ArXiv.org. Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M. & Seifert, C.https://doi.org/10.48550/arXiv.2201.08164Multimodal Machine Learning for 30-Days Post-Operative Mortality Prediction of Elderly Hip Fracture Patients (2022)In Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 (pp. 508-516) (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2021-December). IEEE. Yenidogan, B., Pathak, S., Geerdink, J., Hegeman, J. H. & Van Keulen, M.https://doi.org/10.1109/ICDMW53433.2021.00068

2021

STQS: Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring (2021)Artificial intelligence in medicine, 114. Article 102038. Pathak, S., Lu, C., Belur Nagaraj, S., van Putten, M. J. A. M. & Seifert, C.https://doi.org/10.1016/j.artmed.2021.102038A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports (2021)In 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) (pp. 72-81). Article 9319371. IEEE. Nguyen, E., Theodorakopoulos, D., Pathak, S., Geerdink, J., Vijlbrief, O., van Keulen, M. & Seifert, C.https://doi.org/10.1109/CogMI50398.2020.00019

2020

Human-in-the-loop Language-agnostic Extraction of Medication Data from Highly Unstructured Electronic Health Records (2020)In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 644-650). Article 9346382 (International Conference on Data Mining Workshops (ICDMW); Vol. 2020). IEEE. Ruis, F., Pathak, S., Geerdink, J., Hegeman, J. H., Seifert, C. & van Keulen, M.https://doi.org/10.1109/ICDMW51313.2020.00091

Onderzoeksprofielen

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