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Mattias Rantalainen

Senior Lecturer / Associate Professor

Karolinska Institutet

Sweden

Research Interests

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Professors in Sweden Professors in Medical Imaging Medical Imaging professors in Sweden

Articles (19)

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2026

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2025

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2023

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2023

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2023

Collaborators (15)

Johan Vallon-Christersson

Lund University

SWEDEN

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Pedersen Bodil Ginnerup

DVIP-external, Senior Researcher

Aarhus University

DENMARK

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Svein Reidar Kjosavik

Professor

University of Bergen

NORWAY

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Karina Dalsgaard Sørensen

Aarhus University

DENMARK

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Kimmo Kartasalo

Karolinska Institutet

SWEDEN

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Mark Clements

Karolinska Institutet

SWEDEN

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Balazs Acs

Associate Professor

Karolinska Institutet

SWEDEN

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Kristina Hotakainen

University of Helsinki

FINLAND

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Niccolò Marini

National Institutes of Health

UNITED STATES

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Johan Hartman

Professor at Karolinska Institutet

Karolinska Institutet

SWEDEN

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Tian Mou

Assistant Professor

Shenzhen University

CHINA

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Trung Nghia Vu

Karolinska Institutet

SWEDEN

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Artur Jurgas

AGH University of Science and Technology

POLAND

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Anne-Vibeke Lænkholm

Zealand University Hospital

DENMARK

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Marcin Braun

Medical University of Lodz

POLAND

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Mattias Rantalainen

Research Interests

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Articles (19)

Validation of a deep learning-based AI model for breast cancer risk stratification in postmenopausal ER+/HER2-breast cancer patients

Breaking down the costs for breast cancer: Insights from Sweden's national quality register.

Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies

Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis.

Histological Grade Has Clinical Validity in Neoadjuvant-Treated Breast Cancer: A Multicenter Study

Pathological response to pembrolizumab-based neoadjuvant therapy in ER-low vs. ER-zero breast cancer: a Swedish population-based cohort study

A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur.

AI assessment of tumor-infiltrating lymphocytes on routine H&amp;E-slides as a predictor of response to neoadjuvant therapy in breast cancer-a real-world study.

A method for spatial interpretation of weakly supervised deep learning models in computational pathology

Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology images

The ACROBAT 2022 challenge

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer

Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images

A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

Prediction model for drug response of acute myeloid leukemia patients

Collaborators (15)

Johan Vallon-Christersson

Pedersen Bodil Ginnerup

Svein Reidar Kjosavik

Karina Dalsgaard Sørensen

Kimmo Kartasalo

Mark Clements

Balazs Acs

Kristina Hotakainen

Niccolò Marini

Johan Hartman

Tian Mou

Trung Nghia Vu

Artur Jurgas

Anne-Vibeke Lænkholm

Marcin Braun

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AI assessment of tumor-infiltrating lymphocytes on routine H&E-slides as a predictor of response to neoadjuvant therapy in breast cancer-a real-world study.