261 publications found
Publications
261 publications | 33553 citations
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Liu H, Gao R, Grbic S. AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT. arXiv. Published online March 18, 2025.
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Hendrix W, Hendrix N, Scholten ET, et al.. Artificial intelligence for the detection of airway nodules in chest CT scans. Eur Radiol. Published online March 5, 2025.
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Schuiveling M, Liu H, Eek D, et al.. A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks. GigaScience. 2025;14.
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Botros M, de Boer OJ, Cardenas B, et al.. Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions. Modern Pathology. 2024;37(8):100531.
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de Vente C, Valmaggia P, Hoyng CB, et al.. Generalizable Deep Learning for the Detection of Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy: A MACUSTAR Report. Trans Vis Sci Tech. 2024;13(9):11.
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Höppener DJ, Aswolinskiy W, Qian Z, et al.. Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis. BJS Open. 2024;8(6).
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Magg C, ter Wee MA, Buijs GS, et al.. Towards automation in non-invasive measurement of knee implant displacement. Astley SM, Chen W, eds.. Medical Imaging 2024: Computer-Aided Diagnosis. Published online April 3, 2024:24.
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Schuiveling M, Liu H, Eek D, et al.. A Novel Dataset for Nuclei and Tissue Segmentation in Melanoma with baseline nuclei segmentation and tissue segmentation benchmarks. []. Published online October 8, 2024.
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Legrand L, Tisserand M, Turc G, et al.. Fluid-Attenuated Inversion Recovery Vascular Hyperintensities–Diffusion-Weighted Imaging Mismatch Identifies Acute Stroke Patients Most Likely to Benefit From Recanalization. Stroke. 2016;47(2):424-427.
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Luo S, Yang L, Wang L. Comparison of susceptibility-weighted and perfusion-weighted magnetic resonance imaging in the detection of penumbra in acute ischemic stroke. Journal of Neuroradiology. 2015;42(5):255-260.
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Yu Y, Christensen S, Ouyang J, et al.. Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning. Radiology. 2023;307(1).
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Huijben EMC, Terpstra ML, Galapon AJ, et al.. Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report. Medical Image Analysis. 2024;97:103276.
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Fischer SM, Kiechle J, Lang DM, Peeken JC, Schnabel JA. Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge. Melba. 2024;2(MICCAI 2023 LNQ challenge):798-816.
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Saha A, Bosma JS, Twilt JJ, et al.. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. The Lancet Oncology. 2024;25(7):879-887.PI-CAI: Baseline nnU-Net (supervised) trained on PI-CAI: Private and Public Training Dataset PI-CAI: Baseline nnU-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset PI-CAI: Baseline nnDetection (semi-supervised) trained on PI-CAI: Private and Public Training Dataset PI-CAI: Baseline U-Net (supervised) trained on PI-CAI: Private and Public Training Dataset PI-CAI: Baseline U-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset PI-CAI: Baseline nnDetection (supervised) trained on PI-CAI Private and Public Training Dataset
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Xie W, Jacobs C, Charbonnier J-P, Slebos DJ, van Ginneken B. Emphysema subtyping on thoracic computed tomography scans using deep neural networks. Sci Rep. 2023;13(1).
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Conde-Sousa E, Vale J, Feng M, et al.. HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging. J Imaging. 2022;8(8):213.
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Alves N, Schuurmans M, Rutkowski D, et al.. The PANORAMA Study Protocol: Pancreatic Cancer Diagnosis - Radiologists Meet AI. Zenodo; 2024.
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Hendrix N, Hendrix W, Maresch B, et al.. Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs. Eur Radiol. 2024;34(10):6600-6613.
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van Eekelen L, Spronck J, Looijen-Salamon M, et al.. Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images. Sci Rep. 2024;14(1).
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Yang K, Musio F, Ma Y, et al.. Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA. arXiv. Published online April 30, 2024.
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Peeters D, Alves N, Venkadesh KV, et al.. Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation. Eur Radiol. 2024;34(10):6639-6651.
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Franco-Barranco D, Muñoz-Barrutia A, Arganda-Carreras I. Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes. Neuroinform. 2021;20(2):437-450.
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Franco-Barranco D, Andrés-San Román JA, Hidalgo-Cenalmor I, et al.. BiaPy: Accessible deep learning on bioimages. []. Published online February 5, 2024.
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Huijben EMC, Terpstra ML, Galapon AJ, et al.. Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report. arXiv. Published online June 12, 2024.
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Thummerer A, van der Bijl E, Galapon A Jr, et al.. SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy. Medical Physics. 2023;50(7):4664-4674.
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Schoenpflug LA, Koelzer VH. SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction. arXiv. Published online December 20, 2023.
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Vaidhya Venkadesh K, Aleef TA, Scholten ET, et al.. Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules. Radiology. 2023;308(2).
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van Huizen LMG, Radonic T, van Mourik F, et al.. Compact portable multiphoton microscopy reveals histopathological hallmarks of unprocessed lung tumor tissue in real time. Transl Biophotonics. 2020;2(4).
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Wu Q, Yeo SY, Chen Y, Liu J. Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head. arXiv. Published online October 2, 2023.
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Hendrix W, Hendrix N, Scholten ET, et al.. Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans. Commun Med. 2023;3(1).
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Zhang M, Wu Y, Zhang H, et al.. Multi-site, Multi-domain Airway Tree Modeling. Medical Image Analysis. 2023;90:102957.
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Pinckaers H, Litjens G. Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands. arXiv. Published online October 24, 2019.
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Bosma JS, Saha A, Hosseinzadeh M, Slootweg I, de Rooij M, Huisman H. Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning–based Prostate Cancer Detection Using Biparametric MRI. Radiology: Artificial Intelligence. 2023;5(5).
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Wei D, Lee K, Li H, et al.. AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions. Lecture Notes in Computer Science. Published online 2021:175-185.
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Zhou M, Yuan C, Chen Z, Wang C, Lu Y. Automatic Angle of Progress Measurement of Intrapartum Transperineal Ultrasound Image with Deep Learning. Lecture Notes in Computer Science. Published online 2020:406-414.
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Bai J, Sun Z, Yu S, et al.. A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network. Front Physiol. 2022;13.
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Lu Y, Zhou M, Zhi D, et al.. The JNU-IFM dataset for segmenting pubic symphysis-fetal head. Data in Brief. 2022;41:107904.
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Zhou M, Wang C, Lu Y, et al.. The segmentation effect of style transfer on fetal head ultrasound image: a study of multi-source data. Med Biol Eng Comput. 2023;61(5):1017-1031.
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Lu Y, Zhi D, Zhou M, et al.. Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression. Chiroma H, ed.. Computational and Mathematical Methods in Medicine. 2022;2022:1-14.
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Cipriano M, Allegretti S, Bolelli F, et al.. Deep Segmentation of the Mandibular Canal: A New 3D Annotated Dataset of CBCT Volumes. IEEE Access. 2022;10:11500-11510.
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Mercadante C, Cipriano M, Bolelli F, et al.. A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal. Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Published online 2021:724-731.
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Hamamci IE, Er S, Simsar E, et al.. Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays. arXiv. Published online June 6, 2023.
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Jin Y, Pepe A, Li J, et al.. AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo. arXiv. Published online April 4, 2023.
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Radl L, Jin Y, Pepe A, et al.. AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks. Data in Brief. 2022;40:107801.
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Pepe A, Li J, Rolf-Pissarczyk M, et al.. Detection, segmentation, simulation and visualization of aortic dissections: A review. Medical Image Analysis. 2020;65:101773.
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Ryu J, Puche AV, Shin J, et al.. OCELOT: Overlapped Cell on Tissue Dataset for Histopathology. arXiv. Published online March 27, 2023.
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Wang C-W, Huang C-T, Hsieh M-C, et al.. Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Trans Med Imaging. 2015;34(9):1890-1900.
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Lindner C, Wang C-W, Huang C-T, Li C-H, Chang S-W, Cootes TF. Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms. Sci Rep. 2016;6(1).
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Wang C-W, Huang C-T, Lee J-H, et al.. A benchmark for comparison of dental radiography analysis algorithms. Medical Image Analysis. 2016;31:63-76.
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Alblas D, Brune C, Wolterink J. Deep-learning-based carotid artery vessel wall segmentation in black-blood MRI using anatomical priors. Išgum I, Colliot O, eds.. Medical Imaging 2022: Image Processing. Published online April 4, 2022:24.
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