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Mattias Rantalainen
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2020 – today
- 2024
- [j7]Philippe Weitz
, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo
The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue. Medical Image Anal. 97: 103257 (2024) - [i12]Abhinav Sharma
WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology. CoRR abs/2403.15238 (2024) - [i11]Yujie Xiang, Bojing Liu, Mattias Rantalainen:
Deep Blur Multi-Model (DeepBlurMM) - a strategy to mitigate the impact of image blur on deep learning model performance in histopathology image analysis. CoRR abs/2405.09298 (2024) - [i10]Fredrik K. Gustafsson, Mattias Rantalainen:
Evaluating Deep Regression Models for WSI-Based Gene-Expression Prediction. CoRR abs/2410.00945 (2024) - [i9]Fredrik K. Gustafsson, Mattias Rantalainen:
Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts. CoRR abs/2410.06723 (2024) - 2023
- [i8]Philippe Weitz, Viktoria Sartor, Balazs Acs, Stephanie Robertson, Daniel Budelmann, Johan Hartman, Mattias Rantalainen
Increasing the usefulness of already existing annotations through WSI registration. CoRR abs/2303.06727 (2023) - [i7]Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue. CoRR abs/2305.18033 (2023) - [i6]Xiaoyi Ji, Richard Salmon, Nita Mulliqi, Umair Khan, Yinxi Wang, Anders Blilie, Henrik Olsson, Bodil Ginnerup Pedersen, Karina Dalsgaard Sørensen, Benedicte Parm Ulhøi, Svein Reidar Kjosavik, Emilius A. M. Janssen, Mattias Rantalainen, Lars Egevad, Pekka Ruusuvuori, Martin Eklund, Kimmo Kartasalo
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis. CoRR abs/2307.05519 (2023) - 2022
- [j6]Philippe Weitz
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks. Bioinform. 38(13): 3462-3469 (2022) - [i5]Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo
ACROBAT - a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology. CoRR abs/2211.13621 (2022) - 2021
- [c1]Philippe Weitz
An investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives. ICCVW 2021: 611-619 - [i4]Philippe Weitz, Yinxi Wang, Kimmo Kartasalo
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks. CoRR abs/2104.09310 (2021) - [i3]Bojing Liu, Yinxi Wang, Philippe Weitz, Johan Lindberg, Lars Egevad, Henrik Grönberg, Martin Eklund, Mattias Rantalainen:
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies. CoRR abs/2106.14256 (2021) - 2020
- [i2]Yinxi Wang, Kimmo Kartasalo
Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer. CoRR abs/2009.08917 (2020)
2010 – 2019
- 2019
- [i1]Peter Ström, Kimmo Kartasalo
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence. CoRR abs/1907.01368 (2019) - 2018
- [j5]Trung-Nghia Vu
Isoform-level gene expression patterns in single-cell RNA-sequencing data. Bioinform. 34(14): 2392-2400 (2018) - 2017
- [j4]Ola Spjuth
E-Science technologies in a workflow for personalized medicine using cancer screening as a case study. J. Am. Medical Informatics Assoc. 24(5): 950-957 (2017) - 2016
- [j3]Trung-Nghia Vu
Beta-Poisson model for single-cell RNA-seq data analyses. Bioinform. 32(14): 2128-2135 (2016)
2000 – 2009
- 2008
- [j2]Max Bylesjö, Mattias Rantalainen
K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space. BMC Bioinform. 9 (2008) - [j1]Mattias Rantalainen
Piecewise multivariate modelling of sequential metabolic profiling data. BMC Bioinform. 9 (2008)
Coauthor Index
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