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RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data.

, , , , and . KDD, page 2565-2573. ACM, (2018)

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RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data., , , , and . KDD, page 2565-2573. ACM, (2018)Predicting heart rejection using histopathological whole-slide imaging and deep neural network with dropout., , , and . BHI, page 1-4. IEEE, (2017)Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging., , , and . BHI, page 1-4. IEEE, (2019)Prediction of heart transplant rejection using histopathological whole-slide imaging., , , and . BHI, page 251-254. IEEE, (2018)Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection., , , , , , , , , and 5 other author(s). J. Biomed. Informatics, (March 2023)Rare Heart Transplant Rejection Classification Using Diffusion-Based Synthetic Image Augmentation., , , , , , , , , and . BHI, page 1-4. IEEE, (2023)Improving Heart Transplant Rejection Classification Training using Progressive Generative Adversarial Networks., , , , , , , and . BHI, page 1-4. IEEE, (2021)Automated Classification of Acute Rejection from Endomyocardial Biopsies., , , , , , and . BCB, page 6:1-6:9. ACM, (2020)