AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data
暂无分享,去创建一个
A. L. Wong | M. Guindani | L. Nardo | J. Qi | T. Link | Chih-Chieh Liu | R. Maroldi | K. Elsayes | M. Darrow | S. P. Yap | Q. K. Ng | Sonia Lee | C. Bateni | Y. Abdelhafez | S. Schirò | A. Moawad | Francesco Acquafredda | Dani Sarohia | Michelle Zhang | Layla Shere
[1] Mitchell P. Wilson,et al. Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review , 2022, Skeletal Radiology.
[2] F. Erdoğan,et al. Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning , 2022, Japanese Journal of Radiology.
[3] Xuelei Ma,et al. Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods , 2022, Orphanet Journal of Rare Diseases.
[4] G. Fan,et al. Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics , 2022, Journal of magnetic resonance imaging : JMRI.
[5] Katsuhiro Hayashi,et al. A scoring system combining clinical, radiological, and histopathological examinations for differential diagnosis between lipoma and atypical lipomatous tumor/well-differentiated liposarcoma , 2021, Scientific reports.
[6] D. Hao,et al. Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade. , 2020, AJR. American journal of roentgenology.
[7] B. Ganeshan,et al. Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis , 2020, Skeletal Radiology.
[8] Shao-Feng Duan,et al. Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study. , 2020, Journal of magnetic resonance imaging : JMRI.
[9] A. L. Wong,et al. Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study , 2020, Skeletal Radiology.
[10] X. Montet,et al. Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists , 2020, Sarcoma.
[11] S. Sleijfer,et al. Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI , 2019, The British journal of surgery.
[12] P. Brennan,et al. Effects of time of day on radiological interpretation. , 2019, Clinical radiology.
[13] Julien Cohen-Adad,et al. Deep semantic segmentation of natural and medical images: a review , 2019, Artificial Intelligence Review.
[14] Xiaowei Ding,et al. Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..
[15] Loïc Le Folgoc,et al. Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.
[16] Sin-Ho Jung,et al. The Value of MRI in Distinguishing Subtypes of Lipomatous Extremity Tumors Needs Reassessment in the Era of MDM2 and CDK4 Testing , 2018, Sarcoma.
[17] W. Nailon,et al. Application of Texture Analysis to Study Small Vessel Disease and Blood–Brain Barrier Integrity , 2017, Front. Neurol..
[18] Daniel L. Rubin,et al. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.
[19] Mark J. van der Laan,et al. The relative performance of ensemble methods with deep convolutional neural networks for image classification , 2017, Journal of applied statistics.
[20] S. Wuertzer,et al. Spectrum of Fat-containing Soft-Tissue Masses at MR Imaging: The Common, the Uncommon, the Characteristic, and the Sometimes Confusing. , 2016, Radiographics : a review publication of the Radiological Society of North America, Inc.
[21] Allan Hanbury,et al. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool , 2015, BMC Medical Imaging.
[22] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[23] Christian Szegedy,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[24] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[25] L. White,et al. Can Experienced Observers Differentiate between Lipoma and Well-Differentiated Liposarcoma Using Only MRI? , 2013, Sarcoma.
[26] A. Flanagan,et al. MRI characteristics of lipoma and atypical lipomatous tumor/well-differentiated liposarcoma: retrospective comparison with histology and MDM2 geneamplification , 2013, Skeletal Radiology.
[27] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[28] D. Van dyck,et al. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft‐tissue tumors in T1‐MRI images , 2010, Journal of magnetic resonance imaging : JMRI.
[29] C. Helms,et al. Lipomas, lipoma variants, and well-differentiated liposarcomas (atypical lipomas): results of MRI evaluations of 126 consecutive fatty masses. , 2004, AJR. American journal of roentgenology.
[30] J. Udupa,et al. Standardizing the MR image intensity scales: making MR intensities have tissue-specific meaning , 2000, Medical Imaging.
[31] L G Nyúl,et al. On standardizing the MR image intensity scale , 1999, Magnetic resonance in medicine.
[32] H. Alkadhi,et al. Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images. , 2018, Radiology.
[33] Irina Voiculescu,et al. Family of boundary overlap metrics for the evaluation of medical image segmentation , 2018, Journal of medical imaging.
[34] Arie Nakhmani,et al. Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging. , 2014, Magnetic resonance imaging.