Kimmo Kartasalo
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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
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
Deep learning in image cytometry: a review
A Gupta, PJ Harrison, H Wieslander, N Pielawski, K Kartasalo, G Partel, ...
Cytometry Part A 95 (4), 366-380, 2019
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
Transcriptome Sequencing Reveals PCAT5 as a Novel ERG-Regulated Long Noncoding RNA in Prostate CancerPCAT5: An ERG-Regulated lncRNA
A Ylipää, K Kivinummi, A Kohvakka, M Annala, L Latonen, M Scaravilli, ...
Cancer research 75 (19), 4026-4031, 2015
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
CytoSpectre: a tool for spectral analysis of oriented structures on cellular and subcellular levels
K Kartasalo, RP Pölönen, M Ojala, J Rasku, J Lekkala, K Aalto-Setälä, ...
BMC bioinformatics 16 (1), 1-23, 2015
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
Focal adhesion kinase and ROCK signaling are switch-like regulators of human adipose stem cell differentiation towards osteogenic and adipogenic lineages
L Hyväri, M Ojansivu, M Juntunen, K Kartasalo, S Miettinen, S Vanhatupa
Stem cells international 2018, 2018
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
Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Y Wang, K Kartasalo, P Weitz, B Ács, M Valkonen, C Larsson, ...
Cancer Research 81 (19), 5115–5126, 2021
Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading
L Egevad, D Swanberg, B Delahunt, P Ström, K Kartasalo, H Olsson, ...
Virchows Archiv 477, 777-786, 2020
A durable and biocompatible ascorbic acid-based covalent coating method of polydimethylsiloxane for dynamic cell culture
J Leivo, S Virjula, S Vanhatupa, K Kartasalo, J Kreutzer, S Miettinen, ...
Journal of The Royal Society Interface 14 (132), 20170318, 2017
Dual structured convolutional neural network with feature augmentation for quantitative characterization of tissue histology
M Valkonen, K Kartasalo, K Liimatainen, M Nykter, L Latonen, ...
Proceedings of the IEEE International Conference on Computer Vision …, 2017
Morphological features extracted by AI associated with spatial transcriptomics in prostate cancer
E Chelebian, C Avenel, K Kartasalo, M Marklund, A Tanoglidi, T Mirtti, ...
Cancers 13 (19), 4837, 2021
Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration
K Liimatainen, L Latonen, M Valkonen, K Kartasalo, P Ruusuvuori
BMC cancer 21, 1-14, 2021
Artificial intelligence for diagnosis and Gleason grading of prostate cancer in biopsies—current status and next steps
K Kartasalo, W Bulten, B Delahunt, PHC Chen, H Pinckaers, H Olsson, ...
European Urology Focus 7 (4), 687-691, 2021
The utility of artificial intelligence in the assessment of prostate pathology
L Egevad, P Ström, K Kartasalo, H Olsson, H Samaratunga, B Delahunt, ...
Histopathology 76 (6), 790-792, 2020
Analysis of spatial heterogeneity in normal epithelium and preneoplastic alterations in mouse prostate tumor models
M Valkonen, P Ruusuvuori, K Kartasalo, M Nykter, T Visakorpi, L Latonen
Scientific reports 7 (1), 44831, 2017
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks
P Weitz, Y Wang, K Kartasalo, L Egevad, J Lindberg, H Grönberg, ...
Bioinformatics 38 (13), 3462-3469, 2022
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