Geert Litjens
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    • 🎉 Easily create your own simple yet highly customizable blog
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    • 👩🏼‍🏫 Teach academic courses
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    • Getting Started With Camelyon (Part 1)
    • CAMELYON for Elementary School
  • Experience
  • Experience
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  • Publications
    • Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
    • Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.
    • Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps.
    • Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.
    • The Medical Segmentation Decathlon
    • Deep learning in histopathology: the path to the clinic.
    • Residual cyclegan for robust domain transformation of histopathological tissue slides.
    • Common Limitations of Image Processing Metrics: A Picture Story
    • Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.
    • Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels.
    • Neural Image Compression for Gigapixel Histopathology Image Analysis.
    • Common limitations of performance metrics in biomedical image analysis
    • End-to-end classification on basal-cell carcinoma histopathology whole-slides images
    • Tailoring automated data augmentation to H&E-stained histopathology
    • Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer
    • Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
    • Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
    • The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma.
    • Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma
    • Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
    • Common limitations of performance metrics in biomedical image analysis
    • Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
    • Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images
    • Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks
    • No pixel-level annotations needed
    • Learning to detect lymphocytes in immunohistochemistry with deep learning
    • Neural Image Compression for Gigapixel Histopathology Image Analysis
    • Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.
    • State-of-the-Art Deep Learning in Cardiovascular Image Analysis.
    • A large annotated medical image dataset for the development and evaluation of segmentation algorithms
    • Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
    • A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach.
    • Automated Gleason Grading of Prostate Biopsies Using Deep Learning
    • Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
    • Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification
    • High resolution whole prostate biopsy classification using streaming stochastic gradient descent
    • Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks
    • Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks
    • Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology
    • From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge
    • 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
    • Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
    • Automated segmentation of epithelial tissue in prostatectomy slides using deep learning
    • Automatic color unmixing of IHC stained whole slide images
    • H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection
    • Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images
    • Training convolutional neural networks with megapixel images
    • Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
    • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
    • Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
    • Evaluation of tongue squamous cell carcinoma resection margins using ex-vivo MR.
    • Comparison of Different Methods for Tissue Segmentation In Histopathological Whole-Slide Images
    • The importance of stain normalization in colorectal tissue classification with convolutional networks
    • Using deep learning to segment breast and fibroglandular tissue in MRI volumes
    • A Survey on Deep Learning in Medical Image Analysis
    • Automatic segmentation of histopathological slides from renal allograft biopsies using artificial intelligence
    • Large scale deep learning for computer aided detection of mammographic lesions
    • Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
    • MAGE expression in head and neck squamous cell carcinoma primary tumors, lymph node metastases and respective recurrences: implications for immunotherapy
    • Intranodal signal suppression in pelvic MR lymphography of prostate cancer patients: a quantitative comparison of ferumoxtran-10 and ferumoxytol.
    • Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images
    • Stain specific standardization of whole-slide histopathological images
    • Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients
    • In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide
    • Automated robust registration of grossly misregistered whole-slide images with varying stains
    • Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.
    • Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
    • Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks
    • Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI
    • Multiparametric Magnetic Resonance Imaging for Discriminating Low-Grade From High-Grade Prostate Cancer
    • A multi-scale superpixel classification approach for region of interest detection in whole slide histopathology images
    • Automated detection of prostate cancer in digitized whole-slide images of H&E-stained biopsy specimens
    • Computerized detection of cancer in multi-parametric prostate MRI
    • Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge
    • Computer-aided detection of prostate cancer in MRI
    • Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging
    • Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation
    • Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI
    • Multiparametric MR imaging for the assessment of prostate cancer aggressiveness at 3 Tesla
    • Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy
    • Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3 T
    • Differentiation of Prostatitis and Prostate Cancer by Using Diffusion-weighted MR Imaging and MR-guided Biopsy at 3 T
    • Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?
    • Prostate Cancer localization with a Multiparametric MR Approach (PCaMAP): initial results of a multi-center study
    • Interpatient Variation in Normal Peripheral Zone Apparent Diffusion Coefficient: Effect on the Prediction of Prostate Cancer Aggressiveness
    • Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach
    • A multi-atlas approach for prostate segmentation in MRI
    • A pattern recognition approach to zonal segmentation of the prostate on MRI
    • Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI
    • Dynamic contrast enhanced MR imaging for the assessment of prostate cancer aggressiveness at 3T
    • Automated 3-Dimensional Segmentation of Pelvic Lymph Nodes in Magnetic Resonance Images
    • Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI
    • Detection of Lymph Node Metastases with Ferumoxtran-10 vs Ferumoxytol
    • Differentiation of Normal Prostate Tissue, Prostatitis, and Prostate Cancer: Correlation between Diffusion-weighted Imaging and MR-guided Biopsy
    • Required accuracy of MR-US registration for prostate biopsies
    • Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI
    • Computer aided detection of prostate cancer using T2W, DWI and DCE-MRI: methods and clinical applications
    • Pharmacokinetic models in clinical practice: what model to use for DCE-MRI of the breast?
    • Simulation of nodules and diffuse infiltrates in chest radiographs using CT templates
    • Training a Computer Aided Detection System with Simulated Lung Nodules in Chest Radiographs
    • Pharmacokinetic modeling in breast cancer MRI
    • T1 Quantification: Variable Flip Angle Method vs Use of Reference Phantom
    • Interview with Kijk on AI in Medical Imaging
    • NRC article on artificial intelligence in medicine
    • BNR Wetenschap Vandaag - Interview AI for prostate cancer grading
    • NOS Tech Podcast on AI for Gleason Grading
    • Interview in 'De zomeravond van...' van Omroep P&M
    • Report on Omroep Gelderland on 'De Nieuwe Mens'
    • BNR Wetenschap Vandaag - Zomercollege
    • BNR Beter - Interview
    • Interview with Prostate Cancer Patient Foundation
    • Report on Nieuwsuur about CAMELYON.
    • Interview Regional TV at Alpe Hu'Zes
    • Interview with Vox about FameLab
    • Visit Dutch Prostate Cancer Patient Foundation
    • Artificial Intelligence in Prostate Cancer Diagnostics
    • Applications of Machine Learning for Clinical Practice
    • Introduction to Deep Learning in Medical Imaging
    • Bessensap
    • Dag van de Pathologie
    • The Digital Doctor
    • PhD Thesis Defense

Bessensap

Jun 15, 2018 · 1 min read
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Should doctors fear for their jobs due to the rise of AI? In this presentation I explained which aspects of their job will change and why they should not fear, but welcome the introduction of AI in healthcare.

Last updated on Jun 15, 2018
Geert Litjens
Authors
Geert Litjens
Full Professor of AI for Medical Imaging in Radiology and Pathology
← Introduction to Deep Learning in Medical Imaging Sep 16, 2018
Dag van de Pathologie Apr 13, 2018 →

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