Publications

2025

  1. Dandage, R., Papkov, M., Greco, B.M., Fishman, D., Friesen, H., Wang, K., Styles, E., Kraus, O., Grys, B., Boone, C. and Andrews, B., 2023. Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention. bioRxiv.
  2. Zabolotnii, D., Fishman, D. and Muhammad, N., 2025, February. Automatic Road Boundaries Extraction for High Definition Maps. In 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E) (pp. 1-6). IEEE.
  3. Prytula, Y., Tsiporenko, I., Zeynalli, A. and Fishman, D., 2025. IAUNet: Instance-Aware U-Net. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 4739-4748).
  4. Tsiporenko, I., Chizhov, P. and Fishman, D., 2025. Going beyond u-net: Assessing vision transformers for semantic segmentation in microscopy image analysis. In European Conference on Computer Vision (pp. 222-238). Springer, Cham.

2024

  1. Shvetsov, D., Ariva, J., Domnich, M., Vicente, R. and Fishman, D., 2024, July. COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images. In World Conference on Explainable Artificial Intelligence (pp. 39-59). Cham: Springer Nature Switzerland.

2023

  1. Plutenko, I., Papkov, M., Palo, K., Parts, L. and Fishman, D., 2023, October. Metadata Improves Segmentation Through Multitasking Elicitation. In MICCAI Workshop on Domain Adaptation and Representation Transfer (pp. 147-155). Cham: Springer Nature Switzerland.
  2. Kokol, M., Romano, A., Werner, E., Smrkolj, Š., Roškar, L., Pirš, B., Semczuk, A., Kaminska, A., Adamiak-Godlewska, A., Fishman, D. and Vilo, J., 2023. # 383 BioEndoCar: identifying candidate biomarkers for diagnosis and prognosis of endometrial carcinoma using machine learning and artificial intelligence. International Journal of Gynecological Cancer, 33, p.A368.
  3. Romano, A., Rižner, T.L., Werner, H.M.J., Semczuk, A., Lowy, C., Schröder, C., Griesbeck, A., Adamski, J., Fishman, D. and Tokarz, J., 2023. Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review. Frontiers in Oncology, 13, p.1120178.
  4. Oja, K.T., Ilisson, M., Reinson, K., Muru, K., Reimand, T., Peterson, H., Fishman, D., Esko, T., Haller, T., Kronberg, J. and Wojcik, M.H., 2023. Untargeted metabolomics profiling in pediatric patients and adult populations indicates a connection between lipid imbalance and epilepsy. medRxiv.
  5. Kaliuzhnyi, D., Fishman, D. and Papkov, M., 2023. Reducing the Effect of Incomplete Annotations in Object Detection for Histopathology.

2022

  1. Ali, M.A., Hollo, K., Laasfeld, T., Torp, J., Tahk, M.J., Rinken, A., Palo, K., Parts, L. and Fishman, D., 2022. ArtSeg—Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations. Scientific Reports, 12(1), p.11404.
  2. Tahk, M.J., Torp, J., Ali, M.A., Fishman, D., Parts, L., Grätz, L., Müller, C., Keller, M., Veiksina, S., Laasfeld, T. and Rinken, A., 2022. Live-cell microscopy or fluorescence anisotropy with budded baculoviruses—which way to go with measuring ligand binding to M4 muscarinic receptors?. Open Biology, 12(6), p.220019.

2021

  1. Ali, M.A., Misko, O., Salumaa, S.O., Papkov, M., Palo, K., Fishman, D. and Parts, L., 2021. Evaluating very deep convolutional neural networks for nucleus segmentation from brightfield cell microscopy images. SLAS DISCOVERY: Advancing the Science of Drug Discovery, 26(9), pp.1125-1137.
  2. Fishman, D., Salumaa, S.O., Majoral, D., Laasfeld, T., Peel, S., Wildenhain, J., Schreiner, A., Palo, K. and Parts, L., 2021. Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples. Journal of Microscopy, 284(1), pp.12-24.
  3. Walsh, I., Fishman, D., Garcia-Gasulla, D., Titma, T., Pollastri, G., Harrow, J., Psomopoulos, F.E. and Tosatto, S.C., 2021. DOME: recommendations for supervised machine learning validation in biology. Nature methods, 18(10), pp.1122-1127.
  4. Papkov, M., Roberts, K., Madissoon, L.A., Shilts, J., Bayraktar, O., Fishman, D., Palo, K. and Parts, L., 2021. Noise2Stack: improving image restoration by learning from volumetric data. In Machine Learning for Medical Image Reconstruction: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings 4 (pp. 99-108). Springer International Publishing.

2020

  1. Tampuu, A., Matiisen, T., Semikin, M., Fishman, D. and Muhammad, N., 2020. A survey of end-to-end driving: Architectures and training methods. IEEE Transactions on Neural Networks and Learning Systems, 33(4), pp.1364-1384.
  2. Fishman, D., Kuzmin, I., Adler, P., Vilo, J. and Peterson, H., 2020. PAWER: protein array web exploreR. BMC bioinformatics, 21, pp.1-8.

2019

  1. Knific, T., Fishman, D., Vogler, A., Gstöttner, M., Wenzl, R., Peterson, H. and Rižner, T.L., 2019. Multiplex analysis of 40 cytokines do not allow separation between endometriosis patients and controls. Scientific reports, 9(1), p.16738.
  2. Hertel, C., Fishman, D., Lorenc, A., Ranki, A., Krohn, K., Peterson, P., Kisand, K. and Hayday, A., 2019. Response to comment on’AIRE-deficient patients harbor unique high-affinity disease-ameliorating autoantibodies’. Elife, 8, p.e45826.

2017

  1. Ottas, A., Fishman, D., Okas, T.L., Püssa, T., Toomik, P., Märtson, A., Kingo, K. and Soomets, U., 2017. Blood serum metabolome of atopic dermatitis: Altered energy cycle and the markers of systemic inflammation. PLoS One, 12(11), p.e0188580.
  2. Jones, W., Alasoo, K., Fishman, D. and Parts, L., 2017. Computational biology: deep learning. Emerging Topics in Life Sciences, 1(3), pp.257-274.
  3. Ottas, A., Fishman, D., Okas, T.L., Kingo, K. and Soomets, U., 2017. The metabolic analysis of psoriasis identifies the associated metabolites while providing computational models for the monitoring of the disease. Archives of dermatological research, 309, pp.519-528.
  4. Fishman, D., Kisand, K., Hertel, C., Rothe, M., Remm, A., Pihlap, M., Adler, P., Vilo, J., Peet, A., Meloni, A. and Podkrajsek, K.T., 2017. Autoantibody repertoire in APECED patients targets two distinct subgroups of proteins. Frontiers in immunology, 8, p.976.

2016

  1. Meyer, S., Woodward, M., Hertel, C., Vlaicu, P., Haque, Y., Kärner, J., Macagno, A., Onuoha, S.C., Fishman, D., Peterson, H. and Metsküla, K., 2016. AIRE-deficient patients harbor unique high-affinity disease-ameliorating autoantibodies. Cell, 166(3), pp.582-595.
  2. Meyer, S., Woodward, M., Hertel, C., Vlaicu, P., Haque, Y., Kärner, J., Macagno, A., Onuoha, S.C., Fishman, D. and Peterson, H., APECED patient collaborative. 2016. AIRE-deficient patients harbor unique high-affinity disease-ameliorating autoantibodies. Cell, 166, pp.582-595.