Every reference with a DOI in the deposited reference list resolved to a known
work in Crossref or DataCite at the dated check, and none carried a retraction,
withdrawal, or removal notice.
The 72 checked references that resolve
resolves10.1117/12.2214876Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data
resolves10.1118/1.3528204The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans
resolves10.1117/12.2083124Deep learning with non-medical training used for chest pathology identification
resolves10.1109/IST.2017.8261461Combining convolutional and recurrent neural networks for Alzheimer's disease diagnosis using PET images
resolves10.1038/srep24454Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans
resolves10.1007/978-3-319-46723-8_48Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
resolves10.1109/ISBI.2018.8363547Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
resolves10.1109/TMI.2018.2804799Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT
resolves10.1007/978-3-319-66182-7_83Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks
resolves10.1117/12.2255975Deep residual networks for automatic segmentation of laparoscopic videos of the liver
resolves10.1007/978-3-319-91008-6_63Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
resolves10.1117/12.22557953D convolutional neural network for automatic detection of lung nodules in chest CT
resolves10.1109/TMI.2016.2528162Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
resolves10.1007/s11548-016-1501-5Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets
resolves10.1007/s11548-015-1285-zA geometric method for the detection and correction of segmentation leaks of anatomical structures in volumetric medical images
resolves10.1109/3DV.2016.79V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
resolves10.1007/978-3-319-10404-1_65A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations
resolves10.1007/978-3-319-67389-9_323D U-net with Multi-level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images
resolves10.1109/ISBI.2018.8363540Multi-stream 3D FCN with multi-scale deep supervision for multi-modality isointense infant brain MR image segmentation
resolves10.1109/ICCV.2017.454PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
resolves10.1007/978-3-319-46976-8_12Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting
resolves10.1002/mp.12480Deep learning of the sectional appearances of 3D <scp>CT</scp> images for anatomical structure segmentation based on an <scp>FCN</scp> voting method
The 23 references without a DOI — listed, not checked
no DOI — not checkedAlakwaa W, Nassef M, Badr A: Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). Lung Cancer 8(8): 409, 2017
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no DOI — not checkedCai J, Lu L, Xie Y, Xing F, Yang L (2017) Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function, arXiv: 1707.04912
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no DOI — not checkedCiresan D, Giusti A, Gambardella LM, Schmidhuber J: Deep neural networks segment neuronal membranes in electron microscopy images.. In: Advances in Neural Information Processing Systems, 2012, pp 2843–2851
no DOI — not checkedFakoor R, Ladhak F, Nazi A, Huber M: Using deep learning to enhance cancer diagnosis and classification.. In: Proceedings of the International Conference on Machine Learning, vol 28, 2013
no DOI — not checkedIoffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv: 1502.03167
no DOI — not checkedKamnitsas K, Chen L, Ledig C, Rueckert D, Glocker B: Multi-scale 3D convolutional neural networks for lesion segmentation in brain mri. Ischemic Stroke Lesion Segmentation 13: 46, 2015
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no DOI — not checkedPerez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv: 1712.04621
no DOI — not checkedRoth HR, Oda H, Hayashi Y, Oda M, Shimizu N, Fujiwara M, Misawa K, Mori K (2017) Hierarchical 3D fully convolutional networks for multi-organ segmentation. arXiv: 1704.06382
no DOI — not checkedSimonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556
no DOI — not checkedSrivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R: Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1): 1929–1958, 2014
no DOI — not checkedSrivastava N, Mansimov E, Salakhudinov R: Unsupervised learning of video representations using lstms.. In: International Conference on Machine Learning, 2015, pp 843–852
no DOI — not checkedStollenga MF, Byeon W, Liwicki M, Schmidhuber J: Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation.. In: Advances in Neural Information Processing Systems, 2015, pp 2998–3006
no DOI — not checkedUrban G, Bendszus M, Hamprecht F, Kleesiek J (2014) Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI braTS (Brain Tumor Segmentation) Challenge. Proceedings, winning contribution
no DOI — not checkedXingjian S, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC: Convolutional LSTM network: a machine learning approach for precipitation nowcasting.. In: Advances in Neural Information Processing Systems, 2015, pp 802–810
no DOI — not checkedYosinski J, Clune J, Bengio Y, Lipson H: How transferable are features in deep neural networks?.. In: Advances in Neural Information Processing Systems, 2014, pp 3320–3328
no DOI — not checkedZhou XY, Shen M, Riga C, Yang GZ, Lee SL (2017) Focal FCN: towards small object segmentation with limited training data. arXiv: 1711.01506
no DOI — not checkedZhou Y, Xie L, Shen W, Fishman E, Yuille A (2016) Pancreas segmentation in abdominal CT scan: a coarse-to-fine approach. CoRR arXiv: 1612.08230
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