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Effects of vitamin D supplementation on a deep learning–based mammographic evaluation in SWOG S0812.
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- JNCI Cancer Spectrum, 2024, v. 8, n. 4, p. 1, doi. 10.1093/jncics/pkae042
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Effective Sample Selection and Enhancement of Long Short-Term Dependencies in Signal Detection: HDC-Inception and Hybrid CE Loss.
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- Electronics (2079-9292), 2024, v. 13, n. 16, p. 3194, doi. 10.3390/electronics13163194
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- Article
Uncovering the Atomistic Mechanism for Calcite Step Growth.
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- Angewandte Chemie, 2017, v. 129, n. 29, p. 8584, doi. 10.1002/ange.201701701
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- Article
Deep hash for latent image retrieval.
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- Multimedia Tools & Applications, 2019, v. 78, n. 22, p. 32419, doi. 10.1007/s11042-019-07980-9
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Text recognition in document images obtained by a smartphone based on deep convolutional and recurrent neural network.
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- Multimedia Tools & Applications, 2019, v. 78, n. 18, p. 26453, doi. 10.1007/s11042-019-07855-z
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- Article
A structure-transfer-driven temporal subspace clustering for video summarization.
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- Multimedia Tools & Applications, 2019, v. 78, n. 17, p. 24123, doi. 10.1007/s11042-018-6841-4
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- Article
Sketch recognition using transfer learning.
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- Multimedia Tools & Applications, 2019, v. 78, n. 12, p. 17095, doi. 10.1007/s11042-018-7067-1
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Multi-sensor data fusion for sign language recognition based on dynamic Bayesian network and convolutional neural network.
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- Multimedia Tools & Applications, 2019, v. 78, n. 11, p. 15335, doi. 10.1007/s11042-018-6939-8
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- Article
Small-objectness sensitive detection based on shifted single shot detector.
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- Multimedia Tools & Applications, 2019, v. 78, n. 10, p. 13227, doi. 10.1007/s11042-018-6227-7
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Bi-branch deconvolution-based convolutional neural network for image classification.
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- Multimedia Tools & Applications, 2018, v. 77, n. 23, p. 30233, doi. 10.1007/s11042-018-6130-2
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- Article
Multi-modal gated recurrent units for image description.
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- Multimedia Tools & Applications, 2018, v. 77, n. 22, p. 29847, doi. 10.1007/s11042-018-5856-1
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End-to-end video background subtraction with 3d convolutional neural networks.
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- Multimedia Tools & Applications, 2018, v. 77, n. 17, p. 23023, doi. 10.1007/s11042-017-5460-9
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- Article
Hatching eggs classification based on deep learning.
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- Multimedia Tools & Applications, 2018, v. 77, n. 17, p. 22071, doi. 10.1007/s11042-017-5333-2
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- Article
FDCNet: filtering deep convolutional network for marine organism classification.
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- Multimedia Tools & Applications, 2018, v. 77, n. 17, p. 21847, doi. 10.1007/s11042-017-4585-1
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Semantic image segmentation using fully convolutional neural networks with multi-scale images and multi-scale dilated convolutions.
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- Multimedia Tools & Applications, 2018, v. 77, n. 14, p. 18689, doi. 10.1007/s11042-018-5653-x
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- Article
Copy-move forgery detection based on convolutional kernel network.
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- Multimedia Tools & Applications, 2018, v. 77, n. 14, p. 18269, doi. 10.1007/s11042-017-5374-6
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Feature extraction and classification of VHR images with attribute profiles and convolutional neural networks.
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- Multimedia Tools & Applications, 2018, v. 77, n. 14, p. 18637, doi. 10.1007/s11042-017-5331-4
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- Article
Robust tracking based on H-CNN with low-resource sampling and scaling by frame-wise motion localization.
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- Multimedia Tools & Applications, 2018, v. 77, n. 14, p. 18781, doi. 10.1007/s11042-017-4493-4
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Hyperspectral data classification based on flexible momentum deep convolution neural network.
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- Multimedia Tools & Applications, 2018, v. 77, n. 4, p. 4417, doi. 10.1007/s11042-017-4734-6
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LBP-and-ScatNet-based combined features for efficient texture classification.
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- Multimedia Tools & Applications, 2017, v. 76, n. 21, p. 22425, doi. 10.1007/s11042-017-4824-5
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Image classification based on convolutional neural networks with cross-level strategy.
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- Multimedia Tools & Applications, 2017, v. 76, n. 8, p. 11065, doi. 10.1007/s11042-016-3540-x
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Encryption domain content-based image retrieval and convolution through a block-based transformation algorithm.
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- Multimedia Tools & Applications, 2016, v. 75, n. 21, p. 13805, doi. 10.1007/s11042-015-2917-6
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New adaptive interpolation scheme for image upscaling.
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- Multimedia Tools & Applications, 2016, v. 75, n. 12, p. 7293, doi. 10.1007/s11042-015-2647-9
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MAN and CAT: mix attention to nn and concatenate attention to YOLO.
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- Journal of Supercomputing, 2023, v. 79, n. 2, p. 2108, doi. 10.1007/s11227-022-04726-7
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Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural network.
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- Journal of Supercomputing, 2022, v. 78, n. 4, p. 5029, doi. 10.1007/s11227-021-04041-7
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ST-CAC: a low-cost crosstalk avoidance coding mechanism based on three-valued numerical system.
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- Journal of Supercomputing, 2021, v. 77, n. 7, p. 6692, doi. 10.1007/s11227-020-03527-0
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A convolutional autoencoder-based approach with batch normalization for energy disaggregation.
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- Journal of Supercomputing, 2021, v. 77, n. 3, p. 2961, doi. 10.1007/s11227-020-03375-y
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- Article
A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model.
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- Journal of Supercomputing, 2021, v. 77, n. 1, p. 973, doi. 10.1007/s11227-020-03321-y
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A multipurpose toolkit for teaching DSP in an undergraduate course.
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- Computer Applications in Engineering Education, 2017, v. 25, n. 3, p. 530, doi. 10.1002/cae.21819
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- Article
Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images.
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- European Radiology, 2024, v. 34, n. 7, p. 4218, doi. 10.1007/s00330-023-10518-1
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- Article
Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT.
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- European Radiology, 2024, v. 34, n. 6, p. 3840, doi. 10.1007/s00330-023-10432-6
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Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.
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- European Radiology, 2024, v. 34, n. 5, p. 3431, doi. 10.1007/s00330-023-10306-x
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A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.
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- European Radiology, 2024, v. 34, n. 5, p. 2838, doi. 10.1007/s00330-023-10295-x
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- Article
Revolutionising paediatric radiology: the future impact of artificial intelligence.
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- European Radiology, 2024, v. 34, n. 4, p. 2294, doi. 10.1007/s00330-023-10288-w
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Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients.
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- European Radiology, 2024, v. 34, n. 3, p. 2036, doi. 10.1007/s00330-023-10135-y
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The ecological footprint of medical AI.
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- European Radiology, 2024, v. 34, n. 2, p. 1176, doi. 10.1007/s00330-023-10123-2
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Deep learning enables the differentiation between early and late stages of hip avascular necrosis.
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- European Radiology, 2024, v. 34, n. 2, p. 1179, doi. 10.1007/s00330-023-10104-5
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- Article
Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram.
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- European Radiology, 2023, v. 33, n. 12, p. 9038, doi. 10.1007/s00330-023-09953-x
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- Article
A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.
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- European Radiology, 2023, v. 33, n. 12, p. 9347, doi. 10.1007/s00330-023-09909-1
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- Article
A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data.
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- European Radiology, 2023, v. 33, n. 12, p. 8477, doi. 10.1007/s00330-023-09807-6
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- Article
Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach.
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- European Radiology, 2023, v. 33, n. 10, p. 6746, doi. 10.1007/s00330-023-09668-z
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- Article
Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.
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- European Radiology, 2023, v. 33, n. 9, p. 6548, doi. 10.1007/s00330-023-09827-2
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- Article
Artificial intelligence on MRI for molecular subtyping of diffuse gliomas: feature comparison, visualization, and correlation between radiomics and deep learning.
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- European Radiology, 2022, v. 32, n. 2, p. 745, doi. 10.1007/s00330-021-08400-z
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- Article
Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study.
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- European Radiology, 2022, v. 32, n. 2, p. 1247, doi. 10.1007/s00330-021-08199-9
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Deep learning to automate the labelling of head MRI datasets for computer vision applications.
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- European Radiology, 2022, v. 32, n. 1, p. 725, doi. 10.1007/s00330-021-08132-0
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- Article
Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration.
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- European Radiology, 2022, v. 32, n. 1, p. 384, doi. 10.1007/s00330-021-08121-3
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Delayed brain development of Rolandic epilepsy profiled by deep learning–based neuroanatomic imaging.
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- European Radiology, 2021, v. 31, n. 12, p. 9628, doi. 10.1007/s00330-021-08048-9
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- Article
Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps.
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- European Radiology, 2021, v. 31, n. 12, p. 9012, doi. 10.1007/s00330-021-08036-z
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- Article
Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning–based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.
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- European Radiology, 2021, v. 31, n. 11, p. 8447, doi. 10.1007/s00330-021-08008-3
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Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations.
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- European Radiology, 2021, v. 31, n. 11, p. 8342, doi. 10.1007/s00330-021-07952-4
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