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Titlebook: Innovation in Medicine and Healthcare Systems, and Multimedia; Proceedings of KES-I Yen-Wei Chen,Alfred Zimmermann,Lakhmi C. Jain Conferenc

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31#
發(fā)表于 2025-3-27 00:10:01 | 只看該作者
Success Factors for Realizing Regional Comprehensive Care by EHR with Administrative Datach has not been successful in many countries and regions. We analyze the case of Tamba City, in which an immunization implementation determination system has been implemented by linking medical and government data. Starting with a small, limited scope can allow all stakeholders to experience the sys
32#
發(fā)表于 2025-3-27 02:58:36 | 只看該作者
Clinical Decision-Support System with Electronic Health Record: Digitization of Research in Pharmaields and the healthcare industry. Furthermore, CDS systems are expected to promote and contribute to the drug discovery process in pharmaceutical companies, while the CDS system can be connected and collaborated with electronic health record (EHR). However, current solutions for CDS are not well-es
33#
發(fā)表于 2025-3-27 07:49:51 | 只看該作者
34#
發(fā)表于 2025-3-27 11:25:56 | 只看該作者
Transfer Learning and Fusion Model for Classification of Epileptic PET Imagestial to obtain discriminable features from medical images. The features of the pretraining neural network have been widely used in some image fields. In this paper, we propose a novel fusion modal transfer learning framework by three kinds of two-dimensional convolution networks (ResNet, VGGNet, Inc
35#
發(fā)表于 2025-3-27 15:23:34 | 只看該作者
36#
發(fā)表于 2025-3-27 19:08:50 | 只看該作者
37#
發(fā)表于 2025-3-27 22:40:45 | 只看該作者
Watermarking Algorithm for Encrypted Medical Image Based on DCT-DFRFT they may be vulnerable to malicious attack with poor security. Therefore, this paper studies the digital watermarking algorithm of encrypted medical image based on DCT-DFRFT (Discrete Cosine transform–Discrete Fractional Fourier transform). First, DCT (Discrete Cosine Transform) and tent map are us
38#
發(fā)表于 2025-3-28 05:51:05 | 只看該作者
39#
發(fā)表于 2025-3-28 07:57:46 | 只看該作者
40#
發(fā)表于 2025-3-28 12:32:27 | 只看該作者
Deep Learning for Detecting Breast Cancer Metastases on WSIerefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper, we proposed an improved Deep Learning based classification pipeline for detection of cancer metastases from histological images. The pipeline consists of five stages: 1. Region of Interest (ROI) detection
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