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283 publications found

• Hansen L, Heyer W, Großbröhmer C, et al.. Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges. Published online 2025.
• Hansen L, Heyer W, Großbröhmer C, et al.. Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges. Melba. 2025;3(December 2025):775-791.
  Learn2Reg
• Chen J, Wei S, Honkamaa J, et al.. Beyond the LUMIR challenge: The pathway to foundational registration models. Published online 2025.
  Learn2Reg
• Mojtahedi M, de Vries L, van Poppel L, et al.. Estimation of Acute Infarct Core and Hypoperfused Region from Baseline Noncontrast Computed Tomography and Computed Tomography Angiography Scans of Patients with Ischemic Stroke. SVIN. 2025;5(6).
  CTP-Free Early Stroke Lesion Segmentation from NCCT and CTA
• van Poppel LM, de Vries L, Mojtahedi M, et al.. Machine learning models for CT-based classification of ischemic stroke onset time within or beyond 4.5 h: a comparison of approaches. Eur Radiol. Published online December 8, 2025.
• Imran M, Krebs JR, Cooper MA, Ma J, Zhou Y, Shao W, eds.. Multi-class Segmentation of the Aorta. (Imran M, Krebs JR, Cooper MA, Ma J, Zhou Y, Shao W, eds.). Springer Nature Switzerland; 2026.
  AortaSeg24
• de la Rosa E, Reyes M, Liew S-L, et al.. DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge. Nat Commun. 2025;16(1).
  DeepISLES
• Lems C, Klubíčková N, Brattoli B, et al.. 1371 Towards a multicentric open DigitAL PatHology assIstant beNchmark: Initial Results from the DALPHIN Study. Laboratory Investigation. 2025;105(3):103609.
• Zaghir J, Lokaj B, Kinkel K, et al.. Efficient Clinical Information Extraction from Breast Radiology Reports in French. Studies in Health Technology and Informatics. Published online August 22, 2024.
• Schmid J. Enhancing breast lesion classification by integrating lesion characteristics and clinical data information with ultrafast MRI. European Congress of Radiology. Published online 2024.
• Lv J, Zhu Y, Tenorio CGC, Chohan BS, Eastwood M, Raza SEA. Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images. Lecture Notes in Computer Science. Published online July 17, 2025:58-72.
• Lv J, Nasir ES, Xu K, et al.. KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images. arXiv. Published online October 28, 2025.
• Lokaj B, Durand de Gevigney V, Djema D-A, et al.. Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification. Computers in Biology and Medicine. 2025;188:109721.
• Peeters D, Obreja B, Antonissen N, Jacobs C. Benchmarking of Artificial Intelligence and Radiologists for Lung Cancer Screening in CT: The LUNA25 Challenge. Published online March 27, 2025.
  LUNA25
• Lems C, Tessier L, Bokhorst J-M, et al.. A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides. arXiv. Published online October 3, 2025.
  BEETLE
• Rokuss M, Hamm B, Kirchhoff Y, Maier-Hein K. Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation. arXiv. Published online July 21, 2025.
• Rokuss M, Kirchhoff Y, Akbal S, et al.. LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging. arXiv. Published online March 3, 2025.
• Nechaev D, Pchelnikov A, Ivanova E. HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology. arXiv. Published online May 20, 2025.
• Dörrich M, Balk M, Heusinger T, et al.. A multimodal dataset for precision oncology in head and neck cancer. []. Published online May 31, 2024.
  HANCOTHON25
• Ammeling J, Aubreville M, Banerjee S, et al.. Mitosis Domain Generalization Challenge 2025. Published online March 24, 2025.
  MIDOG2025
• Müller-Franzes G, Sánchez LE, Payne N, et al.. A European Multi-Center Breast Cancer MRI Dataset. arXiv. Published online December 25, 2025.
  ODELIA2025
• Pooch EHP, Agrotis G, Cai L, et al.. Semi-Supervised Learning in Prostate MRI Tumor Segmentation Approaches Fully-Supervised Performance on External Validation. []. Published online May 13, 2025.
• Liu H, Gao R, Krieg E, Grbic S. PanDx: AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT. arXiv. Published online December 16, 2025.
  DTI_PANORAMA
• Hendrix W, Hendrix N, Scholten ET, et al.. Artificial intelligence for the detection of airway nodules in chest CT scans. Eur Radiol. 2025;35(9):5615-5625.
  Airway nodule detection for routine clinical CT scans
• 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.
  PUMA
• 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.
• 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.
• 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).
• 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.
  Tibia bone and implant segmentation
• 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.
• 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.
  CD24
• 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.
  CD24
• 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).
  CD24
• Huijben EMC, Terpstra ML, Galapon AJ, et al.. Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report. Medical Image Analysis. 2024;97:103276.
  SynthRAD2023
• 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.
• 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
• 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).
• 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.
• Alves N, Schuurmans M, Rutkowski D, et al.. The PANORAMA Study Protocol: Pancreatic Cancer Diagnosis - Radiologists Meet AI. Zenodo; 2024.
• 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.
  Carpal instability measurements
• 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).
  Tumor proportion score in non-small cell lung cancer Nuclei detection in immunohistochemistry
• 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 July 9, 2025.
  TopCoW23 topcow24
• 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.
• 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.
• Franco-Barranco D, Andrés-San Román JA, Hidalgo-Cenalmor I, et al.. BiaPy: Accessible deep learning on bioimages. []. Published online February 5, 2024.
• Huijben EMC, Terpstra ML, Galapon AJ, et al.. Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report. arXiv. Published online June 12, 2024.
  SynthRAD2023
• 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.
  SynthRAD2023
• Schoenpflug LA, Koelzer VH. SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction. arXiv. Published online December 20, 2023.
• 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).
  Deep learning to estimate pulmonary nodule malignancy risk using a current and a prior CT image
• 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).