G. Litjens

Latest

• Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.
• Deep learning in histopathology: the path to the clinic.
• Residual cyclegan for robust domain transformation of histopathological tissue slides.
• 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.
• 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
• 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
• 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
• Predicting MYC translocation in HE specimens of diffuse large B-cell lymphoma through deep learning
• 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.
• Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification
• 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
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
• Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
• High resolution whole prostate biopsy classification using streaming stochastic gradient descent
• Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks
• 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
• 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
• 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
• T1 Quantification: Variable Flip Angle Method vs Use of Reference Phantom