標(biāo)題: Titlebook: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; Third International M. Jorge Cardoso,Tal Ar [打印本頁] 作者: T-Lymphocyte 時(shí)間: 2025-3-21 17:43
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影響因子(影響力)
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support影響因子(影響力)學(xué)科排名
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support網(wǎng)絡(luò)公開度
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引頻次
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support被引頻次學(xué)科排名
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support年度引用學(xué)科排名
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support讀者反饋
書目名稱Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support讀者反饋學(xué)科排名
作者: parasite 時(shí)間: 2025-3-21 22:41 作者: 灰姑娘 時(shí)間: 2025-3-22 02:43 作者: HAIRY 時(shí)間: 2025-3-22 05:04
Left Atrium Segmentation in CT Volumes with?Fully Convolutional Networksystems. This paper presents an approach to automatically segmenting left atrium in 3D CT volumes using fully convolutional neural networks (FCNs). We train FCN for automatic segmentation of the left atrium, and then refine the segmentation results of the FCN using the knowledge of the left ventricle作者: 生存環(huán)境 時(shí)間: 2025-3-22 09:49
3D Randomized Connection Network with?Graph-Based Inferencency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are employed as network units to capture the long-term and short-term 3D properties respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we furt作者: 最高峰 時(shí)間: 2025-3-22 13:36 作者: 最高峰 時(shí)間: 2025-3-22 19:12 作者: municipality 時(shí)間: 2025-3-22 22:17
Region-Aware Deep Localization Framework for?Cervical Vertebrae in X-Ray Imagesjury detection system, we propose a localization framework for the cervical spine in X-ray images. The proposed framework employs a segmentation approach to solve the localization problem. As the cervical spine is a single connected component, we introduce a novel region-aware loss function for trai作者: 吸引人的花招 時(shí)間: 2025-3-23 04:09 作者: cochlea 時(shí)間: 2025-3-23 08:09 作者: 猜忌 時(shí)間: 2025-3-23 10:46 作者: 踉蹌 時(shí)間: 2025-3-23 17:50
Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networksd to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in 作者: 果仁 時(shí)間: 2025-3-23 19:00
Non-rigid Craniofacial 2D-3D Registration Using CNN-Based RegressionN regression based non-rigid 2D-3D registration method. An iterative refinement scheme is introduced to update the reference volumetric image and the digitally-reconstructed-radiograph (DRR) for convergence to the target X-ray image. The CNN-based regressor represents the mapping between an image pa作者: expound 時(shí)間: 2025-3-23 22:46
A Deep Level Set Method for Image SegmentationN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during tr作者: Incorporate 時(shí)間: 2025-3-24 04:45
Context-Based Normalization of Histological Stains Using Deep Convolutional Features well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce ., which ext作者: Endometrium 時(shí)間: 2025-3-24 09:37 作者: MUMP 時(shí)間: 2025-3-24 13:55
Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks brain. T2D is a very common metabolic disorder that can cause long term damage to the renal and cardiovascular systems. Previous research has discovered the shape, volume and white matter microstructures in the brain?to be significantly impacted by T2D. We propose a fully-connected deep neural netw作者: 浪費(fèi)物質(zhì) 時(shí)間: 2025-3-24 17:49
Left Atrium Segmentation in CT Volumes with?Fully Convolutional Networks segmented using ASM based method. The proposed FCN models were trained on the STACOM’13 CT dataset. The results show that FCN-based left atrium segmentation achieves Dice coefficient scores over 93% with computation time below 35s per volume, despite of the high variation of LA.作者: 阻塞 時(shí)間: 2025-3-24 22:34
3D Randomized Connection Network with?Graph-Based Inferenceher introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on the publicly available database and results demonstrate that the proposed method can obtain the best performance as compared with other state-of-the-art methods.作者: 要控制 時(shí)間: 2025-3-24 23:48 作者: 無能性 時(shí)間: 2025-3-25 03:56
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.作者: degradation 時(shí)間: 2025-3-25 08:38 作者: 一瞥 時(shí)間: 2025-3-25 14:33
Conference proceedings 2017d at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support..作者: 諂媚于人 時(shí)間: 2025-3-25 18:49 作者: Axillary 時(shí)間: 2025-3-25 19:57 作者: Collected 時(shí)間: 2025-3-26 02:45 作者: Filibuster 時(shí)間: 2025-3-26 05:17 作者: 補(bǔ)助 時(shí)間: 2025-3-26 08:45
JingMin Huang,Gianluca Stringhini,Peng Yong has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.作者: B-cell 時(shí)間: 2025-3-26 13:00
Alessandro Erba,Nils Ole Tippenhaueraining in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is available, outperforming the FCN or the level set model alone.作者: 兵團(tuán) 時(shí)間: 2025-3-26 20:30 作者: indigenous 時(shí)間: 2025-3-26 23:24
0302-9743 MIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support..978-3-319-67557-2978-3-319-67558-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 擔(dān)心 時(shí)間: 2025-3-27 03:44 作者: Aviary 時(shí)間: 2025-3-27 08:22 作者: Ptsd429 時(shí)間: 2025-3-27 11:21 作者: anticipate 時(shí)間: 2025-3-27 17:24 作者: 言行自由 時(shí)間: 2025-3-27 21:29 作者: Pelago 時(shí)間: 2025-3-27 21:58 作者: MORPH 時(shí)間: 2025-3-28 02:06 作者: Anticonvulsants 時(shí)間: 2025-3-28 08:07 作者: 虛假 時(shí)間: 2025-3-28 11:29
Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networksort axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist.作者: 迷住 時(shí)間: 2025-3-28 17:29
Context-Based Normalization of Histological Stains Using Deep Convolutional Features excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.作者: 征兵 時(shí)間: 2025-3-28 20:41
Zixuan Zhao,Yan Wang,Xiaorui Gongng, this loss function is optimised together with the conventional average per-voxel cross entropy loss..The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.作者: 試驗(yàn) 時(shí)間: 2025-3-29 00:20
Xiao Han,Nizar Kheir,Davide Balzarotties and tested on another 124 images, all collected from real life medical emergency rooms. The results show a significant improvement in performance over the previous state-of-the-art cervical vertebrae localization framework.作者: JOG 時(shí)間: 2025-3-29 04:26 作者: 思鄉(xiāng)病 時(shí)間: 2025-3-29 09:56
Lecture Notes in Computer Scienceers of varying sizes to encourage class-specific filters at multiple spatial resolutions. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumors in two independent datasets of scanned histology sections, of which the transition module was superior.作者: Water-Brash 時(shí)間: 2025-3-29 14:29
Adversarial Training and Dilated Convolutions for Brain MRI Segmentationng, this loss function is optimised together with the conventional average per-voxel cross entropy loss..The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.作者: Anticoagulant 時(shí)間: 2025-3-29 16:35
Region-Aware Deep Localization Framework for?Cervical Vertebrae in X-Ray Imageses and tested on another 124 images, all collected from real life medical emergency rooms. The results show a significant improvement in performance over the previous state-of-the-art cervical vertebrae localization framework.作者: parasite 時(shí)間: 2025-3-29 23:01 作者: 辮子帶來幫助 時(shí)間: 2025-3-30 03:52 作者: FICE 時(shí)間: 2025-3-30 05:14 作者: 思想 時(shí)間: 2025-3-30 09:55 作者: 揮舞 時(shí)間: 2025-3-30 12:50
Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networksying a feature selection method. We achieved promising classification performance. Furthermore, in comparison with six (6) classical machine learning algorithms and six (6) alternative deep neural networks similarly tuned for the task, our proposed model outperformed all of them.作者: magenta 時(shí)間: 2025-3-30 20:18 作者: landmark 時(shí)間: 2025-3-30 20:42
Detecting Hardware-Assisted Virtualizationch OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Condition作者: 討好女人 時(shí)間: 2025-3-31 04:06
https://doi.org/10.1007/978-3-031-35504-2ying a feature selection method. We achieved promising classification performance. Furthermore, in comparison with six (6) classical machine learning algorithms and six (6) alternative deep neural networks similarly tuned for the task, our proposed model outperformed all of them.作者: Acquired 時(shí)間: 2025-3-31 08:15
Accelerated Magnetic Resonance Imaging by?Adversarial Neural Network作者: HUSH 時(shí)間: 2025-3-31 10:14
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision SupportThird International 作者: ALIBI 時(shí)間: 2025-3-31 14:31
M. Jorge Cardoso,Tal Arbel,Zhi LuIncludes supplementary material: 作者: HOWL 時(shí)間: 2025-3-31 17:45 作者: 引起痛苦 時(shí)間: 2025-3-31 23:58 作者: SKIFF 時(shí)間: 2025-4-1 03:49
