Computer Vision and Deep Learning for Biomedical Imaging and Radiomics
We apply computer vision techniques for the interpretation of biomedical images and volumes (from electron microscopy, brightfield or fluorescence optical microscopy, x-ray, CT or MRI). Relevant tasks include image classification, 2D and 3D segmentation, detection, tracking, and extraction of low-dimensional features for downstream statistical analysis.
Key Projects
Selected Publications
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M. Veta et al.: “Assessment of algorithms for mitosis detection in breast cancer histopathology images”. Medical Image Analysis. (preprint, pubmed, arxiv).
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A. Giusti, C. Caccia, D. Ciresan, J. Schmidhuber, L. Gambardella: “A Comparison of Algorithms and Humans for Mitosis Detection”. Proc. of International Symposium on Biomedical Imaging (ISBI) 2014. oral presentation.
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D. Ciresan, A. Giusti, L. Gambardella, J. Schmidhuber: “Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks”. Proc. of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2013. Describing our winning entry to ICPR 2012 Mitosis Detection competition. pubmed
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M. Veta, P. J. van Diest, D. Ciresan, A. Giusti, J. Pluim: “Prognostic value of automatic mitosis detection in breast cancer histopathology images”. Proc. of International Symposium on Biomedical Imaging (ISBI) 2015. (abstract).
Contact
Prof. Alessandro Giusti
Professor of AI for Autonomous Robotics at IDSIA