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Weakly Supervised Multimodal 30-Day All-Cause Mortality Prediction for Pulmonary Embolism Patients.

, , , , , , и . ISBI, стр. 1-4. IEEE, (2022)

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Fully Convolutional Network for Liver Segmentation and Lesions Detection., , , , и . LABELS/DLMIA@MICCAI, том 10008 из Lecture Notes in Computer Science, стр. 77-85. (2016)Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results., , , , и . SASHIMI@MICCAI, том 10557 из Lecture Notes in Computer Science, стр. 49-57. Springer, (2017)Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations., , , , , и . Neurocomputing, (2018)Weakly Supervised Attention Model for RV StrainClassification from volumetric CTPA Scans., , , , , , и . CoRR, (2021)Automatic detection and segmentation of liver metastatic lesions on serial CT examinations., , , , и . Medical Imaging: Computer-Aided Diagnosis, том 9035 из SPIE Proceedings, стр. 903519. SPIE, (2014)Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions., , , , , , , и . IEEE J. Biomed. Health Informatics, 20 (6): 1585-1594 (2016)A Simple Free-Text-like Method for Extracting Semi-Structured Data from Electronic Health Records: Exemplified in Prediction of In-Hospital Mortality., , , , , , , , и . Big Data Cogn. Comput., 5 (3): 40 (2021)RV strain classification from 3D CTPA scans using weakly supervised residual attention model., , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 11597 из SPIE Proceedings, SPIE, (2021)Multi-phase liver lesions classification using relevant visual words based on mutual information., , , , и . ISBI, стр. 407-410. IEEE, (2015)Weakly Supervised Multimodal 30-Day All-Cause Mortality Prediction for Pulmonary Embolism Patients., , , , , , и . ISBI, стр. 1-4. IEEE, (2022)