標題: Titlebook: Deep Learning in Healthcare; Paradigms and Applic Yen-Wei Chen,Lakhmi C. Jain Book 2020 Springer Nature Switzerland AG 2020 Deep Learning.M [打印本頁] 作者: 與生 時間: 2025-3-21 17:10
書目名稱Deep Learning in Healthcare影響因子(影響力)
書目名稱Deep Learning in Healthcare影響因子(影響力)學科排名
書目名稱Deep Learning in Healthcare網(wǎng)絡公開度
書目名稱Deep Learning in Healthcare網(wǎng)絡公開度學科排名
書目名稱Deep Learning in Healthcare被引頻次
書目名稱Deep Learning in Healthcare被引頻次學科排名
書目名稱Deep Learning in Healthcare年度引用
書目名稱Deep Learning in Healthcare年度引用學科排名
書目名稱Deep Learning in Healthcare讀者反饋
書目名稱Deep Learning in Healthcare讀者反饋學科排名
作者: 音樂學者 時間: 2025-3-21 22:37
Medical Image Segmentation Using Deep Learningllenges of medical image segmentation, for which actual approaches to overcome those limitations are discussed. Secondly, supervised and semi-supervised architectures are described, where encoder-decoder type networks are the most widely employed ones. Nonetheless, generative adversarial network-bas作者: MIRTH 時間: 2025-3-22 03:42 作者: 宣傳 時間: 2025-3-22 07:06
Medical Image Enhancement Using Deep Learning methods about convolutional layer, deconvolution layer, loss function and evaluation functions for beginners to easily understand. Then, typical state-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network intr作者: Triglyceride 時間: 2025-3-22 09:53 作者: 溝通 時間: 2025-3-22 13:45 作者: 溝通 時間: 2025-3-22 18:19 作者: 富饒 時間: 2025-3-22 22:30 作者: ineluctable 時間: 2025-3-23 05:12
Multi-scale Deep Convolutional Neural Networks for Emphysema Classification and Quantification extracting low-level features or mid-level features without enough high-level information. Moreover, these approaches do not take the characteristics (scales) of different emphysema into account, which are crucial for feature extraction. In contrast to previous works, we propose a novel deep learni作者: Wallow 時間: 2025-3-23 08:33
Opacity Labeling of Diffuse Lung Diseases in CT Images Using Unsupervised and Semi-supervised Learniy deep learning, requires a large number of training data with annotations. Deep learning often requires thousands of training data, but it is tough work for radiologists to give normal and abnormal labels to many images. In this research, aiming the efficient opacity annotation of diffuse lung dise作者: 楓樹 時間: 2025-3-23 12:23 作者: Grievance 時間: 2025-3-23 15:58
Dr. Pecker: A Deep Learning-Based Computer-Aided Diagnosis System in Medical Imagingng on their IT infrastructure design. In comparison with traditional CAD systems that are mostly standalone applications designed to solve a particular task, we explain design choices of a cloud-based CAD platform that allows for running computational intense deep learning algorithms in a cost-effic作者: CLOUT 時間: 2025-3-23 19:27 作者: 倫理學 時間: 2025-3-24 00:47 作者: Biofeedback 時間: 2025-3-24 06:17 作者: Small-Intestine 時間: 2025-3-24 10:24
https://doi.org/10.1007/978-3-658-39879-8e-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network introduced in this chapter, readers can not only make a comparison about the network structure but also have a general understanding about network performance.作者: certain 時間: 2025-3-24 10:46 作者: instructive 時間: 2025-3-24 18:12 作者: 審問,審訊 時間: 2025-3-24 19:58
Erratum to: Theoretische Grundlagen,ed semi-supervised approaches have recently gained the attention of the scientific community. The shift from traditional 2D to 3D architectures is also discussed, as well as the most common loss functions to improve the performance of medical image segmentation approaches. Finally, some future trends and conclusion are described.作者: 同來核對 時間: 2025-3-25 02:16 作者: Pde5-Inhibitors 時間: 2025-3-25 05:49 作者: 雇傭兵 時間: 2025-3-25 11:09 作者: Repetitions 時間: 2025-3-25 11:49
Overcrowding in mature destination images. Then, a landmark-based deep learning framework is presented for AD/MCI classification, by jointly performing feature extraction and classifier training. Experimental results on three public databases demonstrate that the proposed framework boosts the disease diagnosis performance, compared with several state-of-the-art sMRI-based methods.作者: 牽索 時間: 2025-3-25 19:52 作者: 通便 時間: 2025-3-26 00:01 作者: MEET 時間: 2025-3-26 00:46
Opacity Labeling of Diffuse Lung Diseases in CT Images Using Unsupervised and Semi-supervised Learniation for training classifiers. The performance evaluation is carried out by clustering or classification of six kinds of opacities of diffuse lung diseases in computed tomography (CT) images: consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal, and the effectiveness of the proposed methods is clarified.作者: GUILT 時間: 2025-3-26 05:46
Medical Image Classification Using Deep Learninging to classification of focal liver lesions on multi-phase CT images. The main challenge in deep-learning-based medical image classification is the lack of annotated training samples. We demonstrate that fine-tuning can significantly improve the accuracy of liver lesion classification, especially f作者: debble 時間: 2025-3-26 08:53 作者: 施加 時間: 2025-3-26 16:23
Deep Active Self-paced Learning for Biomedical Image Analysisrain it with the DASL strategy. Experimental results show that the proposed models trained with our DASL strategy perform much better than those trained without DASL using the same amount of annotated samples.作者: 抱負 時間: 2025-3-26 20:06
Deep Learning in Textural Medical Image Analysisined feature representations by an activation visualization method, and by measuring the frequency response of trained neural networks, in both qualitative and quantitative ways, respectively. These results demonstrate that such successive transfer learning enables networks to grasp both structural 作者: 結(jié)果 時間: 2025-3-26 22:09 作者: 蒸發(fā) 時間: 2025-3-27 01:11 作者: chandel 時間: 2025-3-27 08:28 作者: Extort 時間: 2025-3-27 09:57 作者: 甜瓜 時間: 2025-3-27 14:46 作者: 傲慢人 時間: 2025-3-27 20:14
https://doi.org/10.1007/978-3-658-28110-6chitecture, optimization algorithm, activation functions and the number of convolution filters. With the designed network, we used relatively less training data than other segmentation methods. The direct output of our network, with no further post-processing, resulted in the dice score of ~99 in tr作者: Hyaluronic-Acid 時間: 2025-3-27 22:33
Overcrowding in mature destinationrain it with the DASL strategy. Experimental results show that the proposed models trained with our DASL strategy perform much better than those trained without DASL using the same amount of annotated samples.作者: Tartar 時間: 2025-3-28 06:01
Im Spannungsfeld von Quantit?t und Qualit?tined feature representations by an activation visualization method, and by measuring the frequency response of trained neural networks, in both qualitative and quantitative ways, respectively. These results demonstrate that such successive transfer learning enables networks to grasp both structural 作者: Herd-Immunity 時間: 2025-3-28 08:08
https://doi.org/10.1007/978-3-8350-5571-1y, based on the classification results, we also perform the quantitative analysis of emphysema in 50 subjects by correlating the quantitative results (the area percentage of each class) with pulmonary functions. We show that centrilobular emphysema (CLE) and panlobular emphysema (PLE) have strong co作者: AVANT 時間: 2025-3-28 11:56 作者: 吸引力 時間: 2025-3-28 15:59 作者: SHOCK 時間: 2025-3-28 22:21 作者: 伴隨而來 時間: 2025-3-29 00:01
Deep Learning in Healthcare978-3-030-32606-7Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: Fecal-Impaction 時間: 2025-3-29 03:46 作者: Merited 時間: 2025-3-29 09:52 作者: Fsh238 時間: 2025-3-29 11:29
Destillier- und Rektifiziertechnikmon deep learning architectures for image detection are briefly explained, including scanning-based methods and end-to-end detection systems. Some considerations about the training scheme and loss functions are also included. Then, an overview of relevant publications in anatomical and pathological 作者: 粗魯?shù)娜?nbsp; 時間: 2025-3-29 17:52
Erratum to: Theoretische Grundlagen,llenges of medical image segmentation, for which actual approaches to overcome those limitations are discussed. Secondly, supervised and semi-supervised architectures are described, where encoder-decoder type networks are the most widely employed ones. Nonetheless, generative adversarial network-bas作者: macrophage 時間: 2025-3-29 20:27
Barbara Neuhofer,Lukas Grundnern. In traditional image classification, low-level or mid-level features are extracted to represent the image and a trainable classifier is then used for label assignments. In recent years, the high-level feature representation of deep convolutional neural networks has proven to be superior to hand-c作者: 冰雹 時間: 2025-3-30 00:51
https://doi.org/10.1007/978-3-658-39879-8 methods about convolutional layer, deconvolution layer, loss function and evaluation functions for beginners to easily understand. Then, typical state-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network intr作者: GLADE 時間: 2025-3-30 06:37
https://doi.org/10.1007/978-3-658-28110-6gh CNNs have achieved state-of-the-art performances, most researches on semantic segmentation using the deep learning methods are in the field of computer vision, so the research on medical images is much less mature than that of natural images, especially, in the field of 3D image segmentation. Our作者: Oration 時間: 2025-3-30 10:31 作者: 鉗子 時間: 2025-3-30 15:17
Im Spannungsfeld von Quantit?t und Qualit?tture domain. This chapter introduces a new transfer learning method, called “two-stage feature transfer,” to analyze textural medical images by deep convolutional neural networks. In the process of the two-stage feature transfer learning, the models are successively pre-trained with both natural ima
