Publications

Peer reviewed publications

    1. Gauraha N, Carlsson L, and Spjuth O. Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery.
      Proceedings of Machine Learning Research. 91, 147-156 (2018).
      URL: http://proceedings.mlr.press/v91/gauraha18a.html
    2. Torruangwatthana T, Wieslander H, Blamey B, Hellander A, and Toor S. HarmonicIO: Scalable data stream processing for scientific datasets, (2018).
      In. proc. IEEE Cloud 2018, San Francisco.
      DOI: 10.1109/CLOUD.2018.00126
    3. Gauraha N, Söderdahl F, and Spjuth O.
      Split Knowledge Transfer in Learning Under Privileged Information Framework.
      Proceedings of Machine Learning Research (PMLR). 105, 43-52. (2019).
      URL: proceedings.mlr.press/v105/gauraha19a.html
    4. Spjuth O, Brännström RC, Carlsson L, and Gauraha N.
      Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets
      Proceedings of Machine Learning Research (PMLR). 105, 53-65. (2019).
      URL: proceedings.mlr.press/v105/spjuth19a.html
    5. Gupta A, Harrison PJ, Wieslander H, Pielawski N, Kartasalo K, Partel G, Solorzano L, Suveer A, Klemm AH, Spjuth O, Sintorn I-M, and Wählby C. Deep Learning in Image Cytometry: A Review. Cytometry A. , Dec 19 2018. DOI:  10.1002/cyto.a.23701
    6. Kensert A, Harrison PJ, Spjuth O
      Transfer learning with deep convolutional neural network for classifying cellular morphological changes. SLAS DISCOVERY: Advancing Life Sciences R&D. 24, 4 (2019). DOI: 10.1177/2472555218818756
    7. Blamey B, Hellander A, and Toor S. Apache Spark Streaming, Kafka and HarmonicIO: A Performance Benchmark and Architecture Comparison for Enterprise and Scientific Computing, in Bench’19, Denver, Colorado, USA, 2019.
      DOI: https://doi.org/10.1007/978-3-030-49556-5_30
      URLs: https://arxiv.org/abs/1807.07724 Slides
    8. Blamey B, Wrede F, Karlsson J, Hellander A, and Toor S. Adapting the Secretary Hiring Problem for Optimal Hot-Cold Tier Placement under Top-K Workloads, CCGrid 2019, Cyprus. DOI: 10.1109/CCGRID.2019.00074
    9. Wieslander H, Harrison P, Skogberg G, Jackson S, Fridén M, Karlsson J, Spjuth O, Wählby C. Deep learning with conformal prediction for hierarchical analysis of large-scale whole-slide tissue images, in IEEE Journal of Biomedical and Health Informatics 2020. DOI: https://doi.org/10.1109/JBHI.2020.2996300
    10. Partel G, Hilscher MM, Milli G, Solorzano L, Klemm AH, Nilsson M, Wählby C. Automated identification of the mouse brain’s spatial compartments from in situ sequencing data. BMC Biol. 2020 Oct 19;18(1):144.  DOI: 10.1186/s12915-020-00874-5. 
    11. Partel G, Wählby C. Spage2vec: Unsupervised representation of localized spatial gene expression signatures. FEBS J. 2020 Sep 25. Epub ahead of print. DOI: 10.1111/febs.15572 . 
    12. Solorzano L, Partel G, Wählby C. TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data. Bioinformatics. 2020 Aug 1;36(15):4363-4365. DOI: 10.1093/bioinformatics/btaa541. 
    13. Stein O, Blamey B, Karlsson J, Sabirsh A, Spjuth O, Hellander A, and Toor S. Smart Resource Management for Data Streaming using an Online Bin-packing  Strategy. 2020 IEEE International Conference on Big Data (Big Data). DOI: https://doi.org/10.1371/journal.pone.0246336
    14. Wieslander H, Wählby C, Sintorn IM. TEM image restoration from fast image streams. PLoS One. 2021 Feb 1;16(2):e0246336.  DOI: 10.1371/journal.pone.0246336. 
    15. Blamey B, Toor S, Dahlö M, Wieslander H, Harrison PJ, Sintorn I-M, Sabirsh A, Wählby C, Spjuth O, and Hellander A. Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit. Gigascience 10(3). 2021. DOI: 10.1093/gigascience/giab018.  
    16. Gauraha N and Spjuth O. Synergy Conformal Prediction for Regression. Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods. vol 1: ICPRAM, 212-221. (2021). DOI: 10.5220/0010229402120221
    17. Alvarsson J, Arvidsson McShane S, Norinder U and Spjuth O. Predicting with confidence: Using conformal prediction in drug discovery. Journal of Pharmaceutical Sciences. 110, 1, 42-49. (2021). DOI: 10.1016/j.xphs.2020.09.055
    18. Harrison PJ, Wieslander H, Sabirsh A,  Karlsson J, Malmsjö V, Hellander A, Wählby C,  Spjuth O. Deep learning models for lipid-nanoparticle-based drug delivery.
      Nanomedicine, Ahead of Print (2021). DOI: 10.2217/nnm-2020-0461 
    19. Morger A, Svensson F, Arvidsson McShane S, Gauraha N, Norinder U, Spjuth O, and Volkamer A. Assessing the Calibration in Toxicological in Vitro Models with Conformal Prediction. Journal of Cheminformatics, 13, 35 (2021).
      DOI: 10.1186/s13321-021-00511-5
    20. Spjuth O, Frid J, and Hellander A. The Machine Learning Life Cycle and the Cloud: Implications for Drug Discovery. Expert Opinion On Drug Discovery, Online ahead of print (2021). DOI: 10.1080/17460441.2021.1932812
    21. Wieslander H, Gupta A, Bergman E, Hallström E, Harrison PJ. Learning to see colours: Biologically relevant virtual staining for adipocyte cell images. PloS one. 2021 Oct 15;16(10):e0258546. DOI: https://doi.org/10.1371/journal.pone.0258546 
    22. Gauraha, N and Spjuth, O. Synergy Conformal Prediction
      Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR. 152, 91-110. (2021).
      URL: proceedings.mlr.press/v152/gauraha21a.html
    23. Norinder U, Spjuth O, Svensson F. Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning. Journal of Cheminformatics. 13, 77 (2021).
      DOI: 10.1186/s13321-021-00555-7
    24. Gupta, A., Sabirsh, A., Wahlby, C., & Sintorn, I. M. SimSearch: A Human-in-the-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images. IEEE Journal of Biomedical and Health Informatics 2022. DOI: 10.1109/JBHI.2022.3177602 
    25. A. Gupta, C. Wählby, and I.M. Sintorn. SimSearch: A Human-in-The-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images. IEEE J Biomed Health Inform. 26(8):4079-4089, (2022). doi: 10.1109/JBHI.2022.3177602
    26. A. Gupta, I.M. Sintorn. Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks. Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, (2022), Proceedings, Part IV. Cham: Springer Nature Switzerland, 2023
    27. A. Gupta, P. J. Harrison, H. Wieslander, J. Rietdijk, J. Carreras Puigvert, P. Georgiev, C. Wählby, O. Spjuth, I. Sintorn. “Is brightfield all you need for mechanism of action prediction?.” bioRxiv (2022): 2022-10.
    28. Zhang T, Gupta A, Rodríguez MAF, Spjuth O, Hellander A and Toor S. Management of Scientific Datasets in Hierarchical Storage Using Reinforcement Learning. 2022 IEEE International Conference on Big Data Workshop. Accepted (2022).
    29. Tian G, Harrison PJ, Sreenivasan AP, Carreras-Puigvert J, Spjuth O. Combining molecular and cell painting image data for mechanism of action prediction. Artificial Intelligence in Life Science. 3, 100060 (2023). DOI: 10.1016/j.ailsci.2023.100060 
    30. Zhang, T., Toor, S., & Hellander, A.. Efficient Hierarchical Storage Management Framework Empowered by Reinforcement Learning. IEEE Transactions on Knowledge and Data Engineering.  (2022) doi: 10.1109/TKDE.2022.3176753
    31. Francisco Rodríguez MA, Carreras-Puigvert J, and Spjuth O
      Designing microplate layouts using artificial intelligence
      Artificial Intelligence in the Life Sciences. 3, 100073 (2023). DOI: 10.1016/j.ailsci.2023.100073
    32. Pielawski, Nicolas, et al. “TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data.” Heliyon 9.5 (2023).

Preprints/papers under review:

  1. T. Zhang, A. Hellander, and S. Toor. Efficient Hierarchical Storage Management Empowered by Reinforcement Learning. Submitted in the IEEE Transactions on Knowledge and Data Engineering.
    Preprint: https://arxiv.org/abs/2201.11668.
    Short Video:     https://www.linkedin.com/feed/update/urn:li:activity:6894217443149975552
  2. Spjuth O, Carlsson L., Gauraha N. Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction. arXiv. 1806.04000 (2018).
    URL: arxiv.org/abs/1806.04000
  3. Blamey B, Sintorn I-M, Hellander A, and Toor S. Resource- and Message Size-Aware Scheduling of Stream Processing at the Edge with application to Real-time Microscopy (Submitted, under Review )
    URL:     https://arxiv.org/abs/1912.09088 , Dataset 

Non-reviewed conference presentations:

  1. Andrea Behanova, Johan Öfverstedt, Ida-Maria Sintorn.  Enhancement of image quality by registration of short exposure images. ( Poster presented at the Swedish Symposium on Deep Learning 2021)
  2. Wieslander H, Gupta A, Bergman E, Hallström E, Harrison PJ. Utilizing privileged information and adversarial training for virtual staining of bright-field images. ( Poster presented at the Swedish Symposium on Deep Learning 2021)

MSc Theses:

  1. Tony Wang: Master Thesis – A Service for Provisioning Compute Infrastructure in the Cloud. URL: http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1352789&dswid=-9414
  2. Oliver Stein: Intelligent Resource Management for Large-scale Data Stream Processing. URL: http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1345975&dswid=-1250 
  3. Lovisa Lugnegård: Master Thesis – Building a High Throughput Microscope Simulator using Apache. URL:  http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1177508&dswid=-2805.