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283 publications found

• Yap MH, Hachiuma R, Alavi A, et al.. Deep learning in diabetic foot ulcers detection: A comprehensive evaluation. Computers in Biology and Medicine. 2021;135:104596.
  DFU2020
• Aubreville M, Bertram C, Veta M, et al.. Quantifying the Scanner-Induced Domain Gap in Mitosis Detection. arXiv. Published online March 31, 2021.
  MIDOG2021
• Aubreville M, Bertram C, Veta M, et al.. MItosis DOmain Generalization Challenge. Published online March 2, 2021.
  MIDOG2021
• van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021;31(6):3797-3804.
• Verma R, Kumar N, Patil A, et al.. MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. IEEE Trans Med Imaging. 2021;40(12):3413-3423.
  MoNuSAC-2020
• Petrick N, Akbar S, Cha KH, et al.. SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment. J Med Imag. 2021;8(03).
  BreastPathQ
• Hennemuth A, Goubergrits L, Ivantsits M, Kuhnigk J-M, eds.. Cerebral Aneurysm Detection. (Hennemuth A, Goubergrits L, Ivantsits M, Kuhnigk J-M, eds.). Springer International Publishing; 2021.
  CADA
• Venkadesh KV, Setio AAA, Schreuder A, et al.. Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT. Radiology. 2021;300(2):438-447.
  Pulmonary Nodule Malignancy Prediction
• Li J, Pimentel P, Szengel A, et al.. AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design. IEEE Trans Med Imaging. 2021;40(9):2329-2342.
  AutoImplant2021
• van Ginneken B, Jacobs C. LUNA16 Part 2/2. Published online March 17, 2019.
  LUNA16
• van Ginneken B, Jacobs C. LUNA16 Part 1/2. Published online March 16, 2019.
  LUNA16
• Ali S, Dmitrieva M, Ghatwary N, et al.. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis. 2021;70:102002.
  EndoCV2022
• González‐Gonzalo C, Sánchez‐Gutiérrez V, Hernández‐Martínez P, et al.. Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration. Acta Ophthalmologica. 2019;98(4):368-377.
• Kavur AE, Gezer NS, Barış M, et al.. CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis. 2021;69:101950.
  CHAOS
• Tellez D, Hoppener D, Verhoef C, et al.. Extending Unsupervised Neural Image Compression With Supervised Multitask Learning. arXiv. Published online April 16, 2020.
• Lessmann N, van Ginneken B, Zreik M, et al.. Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions. IEEE Trans Med Imaging. 2018;37(2):615-625.
• Borovec J, Kybic J, Arganda-Carreras I, et al.. ANHIR: Automatic Non-Rigid Histological Image Registration Challenge. IEEE Trans Med Imaging. 2020;39(10):3042-3052.
  ANHIR
• de Vente C, Boulogne LH, Venkadesh KV, et al.. Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study. arXiv. Published online September 22, 2020.
• Humpire-Mamani GE, Bukala J, Scholten ET, Prokop M, van Ginneken B, Jacobs C. Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning. Radiology: Artificial Intelligence. 2020;2(4):e190102.
• Irvin J, Rajpurkar P, Ko M, et al.. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv. Published online January 23, 2019.
• Maier-Hein KH, Neher PF, Houde J-C, et al.. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun. 2017;8(1).
• 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.