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Deep radiomics: deep learning on radiomics texture images.

, , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 11597 из SPIE Proceedings, SPIE, (2021)

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Matthew B. Hastings

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Matthew Mellon

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Peter Matthew

 

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Iam hiQ - a novel pair of accuracy indices for imputed genotypes., , , , , , , , , и 31 other автор(ы). BMC Bioinform., 23 (1): 50 (2022)Improving malignancy prediction through feature selection informed by nodule size ranges in NLST., , , , , , и . SMC, стр. 1939-1944. IEEE, (2016)Deep radiomics: deep learning on radiomics texture images., , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 11597 из SPIE Proceedings, SPIE, (2021)Representation of Deep Features using Radiologist defined Semantic Features., , , , , , , и . IJCNN, стр. 1-7. IEEE, (2018)Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial., , , , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 9787 из SPIE Proceedings, стр. 97870H. SPIE, (2016)Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future., , , , и . Comput. Biol. Medicine, (2020)Unsupervised Prostate Cancer Histopathology Image Segmentation via Meta-Learning., , , и . CBMS, стр. 838-844. IEEE, (2023)Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening., , , , , и . IEEE Access, (2018)Mitigating Adversarial Attacks on Medical Image Understanding Systems., , , , и . ISBI, стр. 1517-1521. IEEE, (2020)Predicting Nodule Malignancy using a CNN Ensemble Approach., , , , и . IJCNN, стр. 1-8. IEEE, (2018)