Publications

  • Key publications

    INIFY Prostate cancer predictions on biopsies – performance and efficiency study on WSIs from two pathology labs in the US

    Poster presentation at Pathology Visions 2022

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    Evaluation of 3 different scanners’ performance in creating images suitable for INIFY Prostate to accurately predict suspicious cancer areas in prostate biopsies

    Poster presentation Scanner Evaluation, DPA 2021

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    Deep neural network as a decision support tool for the detection of lymph node metastases of colorectal cancer.

    Poster presentation at DPA 2021

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  • Articles

    January 2023

    Predictive uncertainty estimation for out-of-distribution detection in digital pathology

    Medical Image Analysis
    Jasper Linmans, Stefan Elfwing, Jeroen van der Laak, Geert Litjens
    Article (link)
    December 2019

    Clinical-grade Computational Pathology: Alea Iacta Est

    Journal of Pathology Informatics
    Filippo Fraggetta
    Article (link)
    August 2018

    From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

    IEEE Transactions on Medical Imaging
    Peter Bandi, Oscar Geesink, Ludwig Jacobsson, Martin Hedlund et al.
    Article (link)

    Conferences

    2022

    INIFY Prostate cancer predictions on biopsies – performance and efficiency study on WSIs from two pathology labs in the US

    Pathology Visions 2022
    Jennifer Vazzano, Kun Hu, Dorota Johansson, Kristian Eurén, Stefan Elfwing, Ming Zhou, Anil Parwani
    Poster (PDF)
    2021

    Evaluation of 3 different scanners’ performance in creating images suitable for INIFY Prostate to accurately predict suspicious cancer areas in prostate biopsies

    DPA 2021
    Anil Parwani, Dorota Johansson, Kristian Eurén, Lena Kajland Wilén, Ming Zhou
    Abstract (link)
    2021

    Deep neural network as a decision support tool for the detection of lymph node metastases of colorectal cancer

    DPA 2021
    Csaba Kindler, Stefan Elfwing, John Öhrvik, Maziar Nikberg
    Poster (PDF)
    2020

    An AI-based tool to identify cancer areas in lung biopsies

    ECP 2020
    Patrick Micke, Lars Björk, Hedvig Elfving, Stefan Elwing, Mats Andersson, Cecilia Lindskog, Lena Kajland Wilen
    Poster (PDF)
    2018

    Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data

    MIDL 2018, Oral Presentation
    Nikolay Burlutskiy, Nicolas Pinchaud, Feng Gu, Daniel Hägg, Mats Andersson, Lars Björk Kristian Eurén, Cristina Svensson, Lena Kajland Wilén, Martin Hedlund
    Abstract (link)
    2018

    A Deep Learning Framework for Automatic Diagnosis in Lung Cancer

    MIDL 2018
    Nikolay Burlutskiy, Feng Gu, Lena Kajland Wilen, Max Backman, Patrick Micke
    Poster (PDF)
    2018

    Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images

    MICCAI 2018, COMPAY Workshop on computational pathology
    Feng Gu, Nikolay Burlutskiy, Mats Andersson, and Lena Kajland Wilén.
    Abstract (link)
    2018

    A new high-throughput auto-annotation method to detect and outline cancer areas in prostate biopsies

    ECDP 2018, Oral Presentation
    Lars Björk, Jonas Gustafsson, Feria Hikmet Noraddin, Kristian Eurén, Cecilia Lindskog
    Abstract (link)
    2018

    Determining the scale of image patches using a Deep Learning Approach

    ISBI 2018
    Sebastian Otálora, Oscar Perdomo, Manfredo Atzori, Mats Andersson, Ludwig Jacobsson, Martin Hedlund, Henning Müller
    Abstract (link)
    2018

    Tumor proliferation grading from whole slide images

    SPIE 2018
    Mikael Rousson, Martin Hedlund, Mats Andersson, Ludwig Jacobsson, Gunnar Lathen, Bjorn Norell, Oscar Jimenez-del-Toro, Henning Mueller, Manfredo Atzori.
    Abstract (link)
    2017

    Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

    SPIE 2017
    Oscar Jimenez del Toro, Manfredo Atzori, Sebastian Otálora, Mats Andersson, Kristian Eurén, Martin Hedlund, Peter Rönnquist, Henning Müller
    Abstract (link)