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. 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, 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. 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 ChallengesIn Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 (pp. 6665-6673). 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?, 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. 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 PatientsIn Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 (pp. 508-516). 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, 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 ReportsIn 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI), Article 9319371 (pp. 72-81). 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 RecordsIn 2020 International Conference on Data Mining Workshops (ICDMW), Article 9346382 (pp. 644-650). 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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