283 publications found

Publications

283 publications | 40236 citations
  • 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. 2004;5370:648.
  • 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.
  • Maier O, Menze BH, von der Gablentz J, et al.. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Medical Image Analysis. 2017;35:250-269.
  • Chen Q, Xu X, Hu S, Li X, Zou Q, Li Y. A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans. Armato SG, Petrick NA, eds.. SPIE Proceedings. 2017;10134:101344F.
  • Sreekumar A, Nair KR, Sudheer S, Ganesh Nayar H, Nair JJ. Malignant Lung Nodule Detection using Deep Learning. 2020 International Conference on Communication and Signal Processing (ICCSP). Published online July 2020:0209-0212.
  • Hameeteman K, Zuluaga MA, Freiman M, et al.. Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading. Medical Image Analysis. 2011;15(4):477-488.
  • Charbonnier J-P, Brink M, Ciompi F, Scholten ET, Schaefer-Prokop CM, van Rikxoort EM. Automatic Pulmonary Artery-Vein Separation and Classification in Computed Tomography Using Tree Partitioning and Peripheral Vessel Matching. IEEE Trans Med Imaging. 2016;35(3):882-892.
  • Meijering E, Carpenter AE, Peng H, Hamprecht FA, Olivo-Marin J-C. Imagining the future of bioimage analysis. Nat Biotechnol. 2016;34(12):1250-1255.
  • Swiderska-Chadaj Z, Pinckaers H, van Rijthoven M, et al.. Learning to detect lymphocytes in immunohistochemistry with deep learning. Medical Image Analysis. 2019;58:101547.
  • Marstal K, Berendsen F, Dekker N, Staring M, Klein S. The Continuous Registration Challenge: Evaluation-as-a-Service for Medical Image Registration Algorithms. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Published online April 2019:1399-1402.
  • Banerjee N, Sathish R, Sheet D. Deep Neural Architecture for Localization and Tracking of Surgical Tools in Cataract Surgery. Lecture Notes in Computational Vision and Biomechanics. Published online 2019:31-38.
  • Bandi P, Geessink O, Manson Q, et al.. From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge. IEEE Trans Med Imaging. 2019;38(2):550-560.
    CAMELYON17 Tumor Detection in Lymph Nodes
  • Fan K, Wen S, Deng Z. Deep Learning for Detecting Breast Cancer Metastases on WSI. Smart Innovation, Systems and Technologies. Published online 2019:137-145.
  • Laxmisagar HS, Hanumantharaju MC. A Survey on Automated Detection of Breast Cancer based Histopathology Images. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). Published online March 2020:19-24.
  • Armato SG III, Drukker K, Li F, et al.. LUNGx Challenge for computerized lung nodule classification. J Med Imag. 2016;3(4):044506.
  • Zheng X, Sun L, Wang Y, Ding L, Duan Y. Multi-class breast tumor region detection for gigapixel pathology images using deep neural network with rescale approach. Yu H, Pu Y, Li C, Pan Z, eds.. Tenth International Conference on Graphics and Image Processing (ICGIP 2018). Published online May 6, 2019:29.
  • Zhong Z, Li J, Zhang Z, Jiao Z, Gao X. A Coarse-to-Fine Deep Heatmap Regression Method for Adolescent Idiopathic Scoliosis Assessment. Lecture Notes in Computer Science. Published online 2020:101-106.
  • Katz O, Presil D, Cohen L, Schwartzbard Y, Hoch S, Kashani S. Pulmonary-Nodule Detection Using an Ensemble of 3D SE-ResNet18 and DPN68 Models. Lecture Notes in Computer Science. Published online 2020:378-385.
  • Brancati N, Frucci M, Riccio D. Multi-classification of Breast Cancer Histology Images by Using a Fine-Tuning Strategy. Lecture Notes in Computer Science. Published online 2018:771-778.
  • Bron E. Large-scale objective comparison of 29 novel algorithms for computer-aided diagnosis of dementia based on structural MRI. European Congress of Radiology. Published online 2015.
  • Mahbod A, Ellinger I, Ecker R, Smedby Ö, Wang C. Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion. Lecture Notes in Computer Science. Published online 2018:754-762.
  • Wang Y, Sun L, Ma K, Fang J. Breast Cancer Microscope Image Classification Based on CNN with Image Deformation. Lecture Notes in Computer Science. Published online 2018:845-852.
  • Cao H, Bernard S, Heutte L, Sabourin R. Improve the Performance of Transfer Learning Without Fine-Tuning Using Dissimilarity-Based Multi-view Learning for Breast Cancer Histology Images. Lecture Notes in Computer Science. Published online 2018:779-787.
  • Armato SG III, Hadjiiski L, Tourassi GD, et al.. Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. J Med Imag. 2015;2(2):020103.
  • Nazeri K, Aminpour A, Ebrahimi M. Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. Lecture Notes in Computer Science. Published online 2018:717-726.
  • Kwok S. Multiclass Classification of Breast Cancer in Whole-Slide Images. Lecture Notes in Computer Science. Published online 2018:931-940.
  • Grob D, Ginneken B van, Oostveen L, Prokop M, Brink M, Sechopoulos I. [OA136] The lumic challenge: How accurate are registration algorithms in registering lung CT images for subtraction?. Physica Medica. 2018;52:52.
  • Rakhlin A, Shvets A, Iglovikov V, Kalinin AA. Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. Lecture Notes in Computer Science. Published online 2018:737-744.
  • Bernal J, Tajkbaksh N, Sanchez FJ, et al.. Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge. IEEE Trans Med Imaging. 2017;36(6):1231-1249.
  • Karim R, Housden RJ, Balasubramaniam M, et al.. Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge. Journal of Cardiovascular Magnetic Resonance. 2013;15(1):105.
  • Verma R, Kumar N, Abhijeet Patil, Kurian NC, Swapnil Rane, Sethi A. Multi-organ Nuclei Segmentation and Classification Challenge 2020. Unpublished. Published online 2020.
    MoNuSAC-2020