Pekka Ruusuvuori
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
BE Bejnordi, M Veta, PJ Van Diest, B Van Ginneken, N Karssemeijer, ...
Jama 318 (22), 2199-2210, 2017
Critical assessment of automated flow cytometry data analysis techniques
N Aghaeepour, G Finak, FlowCAP Consortium, Dream Consortium, ...
Nature methods 10 (3), 228-238, 2013
Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
P Ström, K Kartasalo, H Olsson, L Solorzano, B Delahunt, DM Berney, ...
The Lancet Oncology 21 (2), 222-232, 2020
Computational framework for simulating fluorescence microscope images with cell populations
A Lehmussola, P Ruusuvuori, J Selinummi, H Huttunen, O Yli-Harja
IEEE transactions on medical imaging 26 (7), 1010-1016, 2007
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
W Bulten, K Kartasalo, PHC Chen, P Ström, H Pinckaers, K Nagpal, Y Cai, ...
Nature medicine 28 (1), 154-163, 2022
The 9th annual MLSP competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment
F Briggs, Y Huang, R Raich, K Eftaxias, Z Lei, W Cukierski, SF Hadley, ...
2013 IEEE international workshop on machine learning for signal processing …, 2013
Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images
J Selinummi, P Ruusuvuori, I Podolsky, A Ozinsky, E Gold, O Yli-Harja, ...
PloS one 4 (10), e7497, 2009
Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images
P Ruusuvuori, T Äijö, S Chowdhury, C Garmendia-Torres, J Selinummi, ...
BMC bioinformatics 11 (1), 1-17, 2010
Probabilistic analysis of gene expression measurements from heterogeneous tissues
T Erkkilä, S Lehmusvaara, P Ruusuvuori, T Visakorpi, I Shmulevich, ...
Bioinformatics 26 (20), 2571-2577, 2010
ANHIR: automatic non-rigid histological image registration challenge
J Borovec, J Kybic, I Arganda-Carreras, DV Sorokin, G Bueno, ...
IEEE transactions on medical imaging 39 (10), 3042-3052, 2020
Simulation of microarray data with realistic characteristics
M Nykter, T Aho, M Ahdesmäki, P Ruusuvuori, A Lehmussola, O Yli-Harja
BMC bioinformatics 7 (1), 1-17, 2006
Evaluating the performance of microarray segmentation algorithms
A Lehmussola, P Ruusuvuori, O Yli-Harja
Bioinformatics 22 (23), 2910-2917, 2006
Metastasis detection from whole slide images using local features and random forests
M Valkonen, K Kartasalo, K Liimatainen, M Nykter, L Latonen, ...
Cytometry Part A 91 (6), 555-565, 2017
Benchmark set of synthetic images for validating cell image analysis algorithms
P Ruusuvuori, A Lehmussola, J Selinummi, T Rajala, H Huttunen, ...
2008 16th European Signal Processing Conference, 1-5, 2008
Synthetic images of high-throughput microscopy for validation of image analysis methods
A Lehmussola, P Ruusuvuori, J Selinummi, T Rajala, O Yli-Harja
Proceedings of the IEEE 96 (8), 1348-1360, 2008
Cytokeratin-supervised deep learning for automatic recognition of epithelial cells in breast cancers stained for ER, PR, and Ki-67
M Valkonen, J Isola, O Ylinen, V Muhonen, A Saxlin, T Tolonen, M Nykter, ...
IEEE transactions on medical imaging 39 (2), 534-542, 2019
Virtual cell imaging: A review on simulation methods employed in image cytometry
V Ulman, D Svoboda, M Nykter, M Kozubek, P Ruusuvuori
Cytometry Part A 89 (12), 1057-1072, 2016
Simulating fluorescent microscope images of cell populations
A Lehmussola, J Selinummi, P Ruusuvuori, A Niemisto, O Yli-Harja
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 3153-3156, 2006
Predicting molecular phenotypes from histopathology images: A transcriptome-wide expression–morphology analysis in breast cancer
Y Wang, K Kartasalo, P Weitz, B Acs, M Valkonen, C Larsson, ...
Cancer research 81 (19), 5115-5126, 2021
Comparative analysis of tissue reconstruction algorithms for 3D histology
K Kartasalo, L Latonen, J Vihinen, T Visakorpi, M Nykter, P Ruusuvuori
Bioinformatics 34 (17), 3013-3021, 2018
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