Artificial Intelligence and Machine Learning for Healthcare
Vol. 2: Emerging Methodologies and Trends
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Yen-Wei Chen
Health Informatics
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Chapter and Conference Paper
Robust Zero-Watermarking Algorithm for Medical Images Based on SUSAN-DCT
With the digitization of medical systems, the information security of medical images faces great risks. In order to protect the personal information of patients contained in medical images, this paper proposes...
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Chapter and Conference Paper
An Improved Multi-Instance Learning Model for Postoperative Early Recurrence Prediction of Hepatocellular Carcinoma Using Histopathological Images
HCC(Hepatocellular carcinoma) is one of the primary liver cancer around the world, and it produces a high mortality rate in clinical. Surgical resection is the first-line treatment choice for patients with HCC...
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Chapter and Conference Paper
An Efficient Dual-Pooling Channel Attention Module for Weakly-Supervised Liver Lesion Classification on CT Images
The classification of liver lesions on computed tomography (CT) images is vital for early liver cancer diagnosis. To aid physicians, several methods have been proposed to perform that task automatically. These...
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Chapter and Conference Paper
Robust Zero Watermarking Algorithm for Medical Images Based on BRISK-FREAK and DCT
With the development of mobile Internet technology, the technology in the field of medical image processing is constantly updated and iterated. Digital watermarking technology plays an important role in the fi...
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Chapter and Conference Paper
Deep Neural Network-Based Classification of Focal Liver Lesions Using Phase-Shuffle Prediction Pre-training
Computer-aided diagnosis plays an important role in focal liver lesion diagnosis (classification) which is one of the leading causes of death worldwide. Due to their superior performance, deep learning-based f...
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Chapter and Conference Paper
A Robust Watermarking Algorithm for Medical Images Based on DWT-Daisy and Discrete Cosine Transform
With the development of digital medical imaging technology, the transmission of medical data in the network is becoming more and more frequent. In order to protect patients’ privacy information during transmis...
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Chapter and Conference Paper
Teacher-Student Learning Using Focal and Global Distillation for Accurate Cephalometric Landmark Detection
Cephalometric analysis is used frequently by dentists and orthodontists to analyze the role of the human skull in making a diagnosis and formulating treatment plans. To perform such analysis, cephalometric lan...
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Chapter and Conference Paper
Robust Watermarking Algorithm for Medical Volume Data Based on PJFM and 3D-DCT
With the rapid development of information technology and digital image processing, digital imaging technology is gradually mature and widely used in the field of medicine. The use of computed tomography (CT) a...
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Book
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Book
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Book and Conference Proceedings
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Chapter
Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images
For computer-aided diagnosis, automatic and accurate liver tumor detection in multi-phase CT images is essential. Nowadays, deep learning has been widely used in various medical applications. Deep learning-bas...
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Chapter and Conference Paper
Unsupervised Domain Adaptation with Adversarial Learning for Liver Tumors Detection in Multi-phase CT Images
Automatic and accurate liver tumor detection in multi-phase CT images plays an important role in computer-aided diagnosis. A deep learning-based AI system requires a large amount of training data, but sufficie...
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Chapter and Conference Paper
Deep Liver Lesion AI System: A Liver Lesion Diagnostic System Using Deep Learning in Multiphase CT
In the medical field, computer-aided diagnosis (CAD) systems are an important tool that can assist doctors in the diagnosis process. However, the powerful higher performance and accuracy models in CAD systems ...
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Chapter and Conference Paper
Improved Mask R-CNN with Deformable Convolutions for Accurate Liver Lesion Detection in Multiphase CT Images
Multiphase computed tomography (CT) is widely used for the diagnosis of liver lesions. Accurate detection of lesions in multiphase CT images is important in the diagnosis of liver cancer. However, to detect li...
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Chapter and Conference Paper
Robust Zero-Watermarking Algorithm for Medical Images Based on Residual Network and Depthwise Separable Convolutions
Using traditional watermarking technology to embed watermark in the carrier image will destroy the pixel information and is not suitable for the privacy protection of medical images. Based on this, we proposed...
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Chapter and Conference Paper
A Unified Framework for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Multi-phase CT Images
Early recurrence (ER) after surgery is one of the main causes of death in Hepatocellular Carcinoma (HCC). Making an effective prediction of the risk of ER is important in develo** treatment strategies. The e...
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Chapter
Sparse Modeling in Analysis for Multidisciplinary Medical Data
Multi-phase CT images, which is also known as dynamic CT images, are widely used for the diagnosis of focal liver lesions. In addition to 3-dimensional spatial information, the multi-phase CT image also has te...
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Chapter
Radiomics and Its Application in Predicting Microvascular Invasion of Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is a global health concern, with increasing morbidity and mortality rates in the world. Microvascular invasion (MVI) is a major cause of early postoperative recurrence of HCC. Pr...
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Chapter
Computer-Aided Detection of Depressive Severity Using Multimodal Behavioral Data
This chapter presents depressive severity detection using deep learning methods for automatic audio, visual and audiovisual emotion sensing. The article starts from basic methods on experimental design and dat...
