262 publications found

Publications

262 publications | 33918 citations
  • Nath V, Schilling KG, Parvathaneni P, et al.. Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge. Magnetic Resonance Imaging. 2019;51(1):234-249.
  • Bernard O, Lalande A, Zotti C, et al.. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?. IEEE Trans Med Imaging. 2018;37(11):2514-2525.
  • Suinesiaputra A, Cowan BR, Al-Agamy AO, et al.. A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Medical Image Analysis. 2014;18(1):50-62.
  • Rueda S, Fathima S, Knight CL, et al.. Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge. IEEE Trans Med Imaging. 2014;33(4):797-813.
  • Veta M, van Diest PJ, Willems SM, et al.. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis. 2015;20(1):237-248.
  • Maška M, Ulman V, Svoboda D, et al.. A benchmark for comparison of cell tracking algorithms. Bioinformatics. 2014;30(11):1609-1617.
  • Yang X, Wu N, Cheng G, et al.. Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics. 2014;90(5):1225-1233.
  • Arganda-Carreras I, Turaga SC, Berger DR, et al.. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat. 2015;9.
  • Ulman V, Maška M, Magnusson KEG, et al.. An objective comparison of cell-tracking algorithms. Nat Methods. 2017;14(12):1141-1152.
  • Carass A, Roy S, Jog A, et al.. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. NeuroImage. 2017;148:77-102.
  • Jimenez-del-Toro O, Muller H, Krenn M, et al.. Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks. IEEE Trans Med Imaging. 2016;35(11):2459-2475.
  • Crimi A, Menze B, Maier O, Reyes M, Winzeck S, Handels H, eds.. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. (Crimi A, Menze B, Maier O, Reyes M, Winzeck S, Handels H, eds.). Springer International Publishing; 2016.
  • Tobon-Gomez C, De Craene M, McLeod K, et al.. Benchmarking framework for myocardial tracking and deformation algorithms: An open access database. Medical Image Analysis. 2013;17(6):632-648.
  • Sirinukunwattana K, Snead DRJ, Rajpoot NM. A Stochastic Polygons Model for Glandular Structures in Colon Histology Images. IEEE Trans Med Imaging. 2015;34(11):2366-2378.
  • Marinescu RV, Oxtoby NP, Young AL, et al.. The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up. arXiv. Published online December 30, 2021.
    TADPOLE
  • Marinescu RV, Oxtoby NP, Young AL, et al.. TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease. arXiv. Published online August 14, 2019.
    TADPOLE
  • Allan M, Shvets A, Kurmann T, et al.. 2017 Robotic Instrument Segmentation Challenge. arXiv. Published online February 22, 2019.
  • Heller N, Isensee F, Maier-Hein KH, et al.. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge. arXiv. Published online August 11, 2020.
    KiTS19
  • Ali S. Endoscopy Artefact Detection (EAD) Dataset. Published online March 1, 2019.
    EndoCV2022
  • Pedrosa J, Aresta G, Ferreira C, et al.. LNDb: A Lung Nodule Database on Computed Tomography. arXiv. Published online December 20, 2019.
    LNDb
  • Leuschner J, Schmidt M, Baguer DO, Maass P. LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction. Sci Data. 2021;8(1).
    lodopab
  • Li J, Pepe A, Gsaxner C, von Campe G, Egger J. A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge. arXiv. Published online June 25, 2020.
    AutoImplant
  • Prokop M, van Everdingen W, van Rees Vellinga T, et al.. CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19—Definition and Evaluation. Radiology. 2020;296(2):E97-E104.
  • Moore PJ, Lyons TJ, Gallacher J, for the Alzheimer’s Disease Neuroimaging Initiative. Random forest prediction of Alzheimer’s disease using pairwise selection from time series data. Ginsberg SD, ed.. PLoS ONE. 2019;14(2):e0211558.
  • Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al.. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199.
    CAMELYON16
  • Lessmann N, van Ginneken B, de Jong PA, Išgum I. Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Medical Image Analysis. 2019;53:142-155.
    Vertebra segmentation and labeling
  • Sogancioglu E, Murphy K, Calli E, Scholten ET, Schalekamp S, Van Ginneken B. Cardiomegaly Detection on Chest Radiographs: Segmentation Versus Classification. IEEE Access. 2020;8:94631-94642.
    CXR Cardiomegaly Detection
  • Lessmann N, Sánchez CI, Beenen L, et al.. Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence. Radiology. 2021;298(1):E18-E28.
    CORADS-AI
  • Stephan CN, D’Alonzo SS, Wilson EK, Guyomarc’h P, Berg GE, Byrd JE. Skeletal Identification by Radiographic Comparison of the Cervicothoracic Region on Chest Radiographs a , b. New Perspectives in Forensic Human Skeletal Identification. Published online 2018:277-292.
  • Rubens U, Mormont R, Paavolainen L, et al.. BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows. Patterns. 2020;1(3):100040.
  • Zhao L, Yan Z, Wolf M, Zhao Y, Zhan Y. A deep-learning based automatic pulmonary nodule detection system. Mori K, Petrick N, eds.. Medical Imaging 2018: Computer-Aided Diagnosis. Published online February 27, 2018:114.
  • Marami B, Prastawa M, Chan M, Donovan M, Fernandez G, Zeineh J. Ensemble Network for Region Identification in Breast Histopathology Slides. Lecture Notes in Computer Science. Published online 2018:861-868.
  • Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475(2):131-138.
  • Greenspan H, van Ginneken B, Summers RM. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Trans Med Imaging. 2016;35(5):1153-1159.
  • Porwal P, Pachade S, Kamble R, et al.. Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research. Data. 2018;3(3):25.
  • Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Science. 2020;111(5):1452-1460.
  • Armato SG, Huisman H, Drukker K, et al.. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imag. 2018;5(04):1.
    nnUNet semi-supervised (trained w/ external public dataset [ProstateX])
  • Narayanan BN, Hardie RC, Kebede TM. Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses. J Med Imag. 2018;5(01):1.
  • Zisimopoulos O, Flouty E, Luengo I, et al.. DeepPhase: Surgical Phase Recognition in CATARACTS Videos. Lecture Notes in Computer Science. Published online 2018:265-272.
  • Marée R. Open Practices and Resources for Collaborative Digital Pathology. Front Med. 2019;6.
  • Chennamsetty SS, Safwan M, Alex V. Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks. Lecture Notes in Computer Science. Published online 2018:804-811.
  • Mendrik AM, Vincken KL, Kuijf HJ, et al.. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans. Computational Intelligence and Neuroscience. 2015;2015:1-16.
  • Veta M, Heng YJ, Stathonikos N, et al.. Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Medical Image Analysis. 2019;54:111-121.
  • Müller H. Medical Image Retrieval: Applications and Resources. Proceedings of the 2020 International Conference on Multimedia Retrieval. Published online June 8, 2020:2-3.
  • Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff MD. Comparative study of retinal vessel segmentation methods on a new publicly available database. Fitzpatrick JM, Sonka M, eds.. SPIE Proceedings. Published online May 12, 2004.
  • Schaap M, Metz CT, van Walsum T, et al.. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical Image Analysis. 2009;13(5):701-714.
  • Niemeijer M, van Ginneken B, Cree MJ, et al.. Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Trans Med Imaging. 2010;29(1):185-195.
  • Menze BH, Jakab A, Bauer S, et al.. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):1993-2024.
  • Chenouard N, Smal I, de Chaumont F, et al.. Objective comparison of particle tracking methods. Nat Methods. 2014;11(3):281-289.
  • Figueira G, Wang Y, Sun L, Zhou H, Zhang Q. Adversarial-Based Domain Adaptation Networks for Unsupervised Tumour Detection in Histopathology. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). Published online April 2020:1284-1288.