標(biāo)題: Titlebook: Machine Learning in Medical Imaging; 10th International W Heung-Il Suk,Mingxia Liu,Chunfeng Lian Conference proceedings 2019 Springer Natur [打印本頁] 作者: memoir 時(shí)間: 2025-3-21 16:44
書目名稱Machine Learning in Medical Imaging影響因子(影響力)
書目名稱Machine Learning in Medical Imaging影響因子(影響力)學(xué)科排名
書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度
書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning in Medical Imaging被引頻次
書目名稱Machine Learning in Medical Imaging被引頻次學(xué)科排名
書目名稱Machine Learning in Medical Imaging年度引用
書目名稱Machine Learning in Medical Imaging年度引用學(xué)科排名
書目名稱Machine Learning in Medical Imaging讀者反饋
書目名稱Machine Learning in Medical Imaging讀者反饋學(xué)科排名
作者: indecipherable 時(shí)間: 2025-3-21 22:48
Conference proceedings 2019ICCAI 2019, in Shenzhen, China, in October 2019.?.The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions.?.They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt w作者: Inkling 時(shí)間: 2025-3-22 01:34
Conference proceedings 2019ng, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.?.作者: Ardent 時(shí)間: 2025-3-22 08:22 作者: osculate 時(shí)間: 2025-3-22 09:19 作者: Misnomer 時(shí)間: 2025-3-22 13:42
WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histhe pathology hierarchical relationships between pixels in each patch. By aggregating patch segmentation results from WSI-Net, we generate a segmentation map for the WSI and extract its morphological features for WSI-level classification. Experimental results show that our WSI-Net can be ., . and . on our benchmark dataset.作者: Cryptic 時(shí)間: 2025-3-22 20:07 作者: 信條 時(shí)間: 2025-3-22 21:16
MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network,ial multi-modal input images, and (3) a densely-dilated U-Net as the encoder-decoder backbone for image segmentation. Experiments on ISLES 2018 data set have shown that MSAFusionNet achieves the state-of-the-art segmentation accuracy.作者: CURT 時(shí)間: 2025-3-23 01:21 作者: rectocele 時(shí)間: 2025-3-23 08:18 作者: 忍受 時(shí)間: 2025-3-23 18:52 作者: 大廳 時(shí)間: 2025-3-23 23:23
Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation,ral popular deep learning-based models both qualitatively and quantitatively. The superiority of the new model is substantiated based on a series of rigorous experiments using a multi-modal MRI database composed of 355 real demented patients in this study, from the statistical perspective.作者: LUDE 時(shí)間: 2025-3-24 04:48 作者: 刺耳 時(shí)間: 2025-3-24 08:19 作者: aneurysm 時(shí)間: 2025-3-24 11:09 作者: Curmudgeon 時(shí)間: 2025-3-24 17:25
Advancing Pancreas Segmentation in Multi-protocol MRI Volumes Using Hausdorff-Sine Loss Function, in magnetic resonance imaging (MRI) remains challenging due to high inter-patient variability. Also, the resolution and speed of MRI scanning present artefacts that blur the pancreas boundaries between overlapping anatomical structures. This paper proposes a dual-stage automatic segmentation method作者: Herpetologist 時(shí)間: 2025-3-24 22:37
WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Hisches from the WSI into three types, including non-malignant, ductal carcinoma in situ, and invasive ductal carcinoma. It adds a parallel classification branch on the top of the low layer of a semantic segmentation model DeepLab. This branch can fast identify and discard those non-malignant patches i作者: Hypopnea 時(shí)間: 2025-3-25 02:43 作者: visceral-fat 時(shí)間: 2025-3-25 04:22
MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network,as not been fully studied in the field of deep learning within such a context. In this paper, we address the task of end-to-end segmentation based on multi-modal data and propose a novel deep learning framework, multiple subspace attention-based deep multi-modal fusion network (referred to as MSAFus作者: 花爭(zhēng)吵 時(shí)間: 2025-3-25 11:34
DCCL: A Benchmark for Cervical Cytology Analysis, fields, including cervical cytology, a large well-annotated benchmark dataset remains missing. In this paper, we introduce by far the largest cervical cytology dataset, called Deep Cervical Cytological Lesions (referred to as DCCL). DCCL contains 14,432 image patches with around . pixels cropped fr作者: 盡責(zé) 時(shí)間: 2025-3-25 12:33 作者: 無可非議 時(shí)間: 2025-3-25 18:44
,Children’s Neuroblastoma Segmentation Using Morphological Features,ldren. However, the automatic segmentation of NB on CT images has been addressed weakly, mostly because children’s CT images have much lower contrast than adults, especially those aged less than one year. Furthermore, neuroblastomas can develop in different body parts and are usually in variable siz作者: Irrepressible 時(shí)間: 2025-3-25 20:48 作者: SEED 時(shí)間: 2025-3-26 02:55
Deep Active Lesion Segmentation,oundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities作者: Oscillate 時(shí)間: 2025-3-26 06:52 作者: 十字架 時(shí)間: 2025-3-26 10:03 作者: 咒語 時(shí)間: 2025-3-26 12:39
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation,erformance for organ segmentation has been achieved by deep learning models, ...., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducin作者: 外來 時(shí)間: 2025-3-26 19:55
Privacy-Preserving Federated Brain Tumour Segmentation,r training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Altho作者: 沙文主義 時(shí)間: 2025-3-26 21:04
Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Hisms are based on the assumption that the nuclei center should have larger responses than their surroundings in the probability map of the pathological image, which in turn transforms the detection or localization problem into finding the local maxima on the probability map. However, all the existing 作者: amphibian 時(shí)間: 2025-3-27 04:48
Semi-supervised Multi-task Learning with Chest X-Ray Images,ntrast, generative modeling—i.e., learning data generation and classification—facilitates semi-supervised training with limited labeled data. Moreover, generative modeling can be advantageous in accomplishing multiple objectives for better generalization. We propose a novel multi-task learning model作者: Narrative 時(shí)間: 2025-3-27 06:12 作者: 和藹 時(shí)間: 2025-3-27 09:46
Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization,pixel-level segmentation task. In experiments we validate our method by segmenting gray matter, white matter, and several major regions on a challenge dataset. The proposed method with only seven subjects for training can achieve 84.46% of Dice score in the onsite test set.作者: 山頂可休息 時(shí)間: 2025-3-27 17:02 作者: 運(yùn)動(dòng)的我 時(shí)間: 2025-3-27 18:45 作者: Genetics 時(shí)間: 2025-3-27 22:04
,Children’s Neuroblastoma Segmentation Using Morphological Features,lect 248 CT scans from distinct patients with manually-annotated labels to establish a dataset for NB segmentation. Our method is evaluated on this dataset as well as the public Brats2018, and experimental results shows that the morphological constraints can improve the performance of medical image 作者: HAIRY 時(shí)間: 2025-3-28 02:11
GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for Automatic Detection of Esophageal Abnormascopic image and the generated GF image separately; the DenseNet provides a reduction in the trained parameters while supporting the network accuracy and enables a maximum flow of information. Features extracted from the GF and endoscopic images are fused through bilinear fusion before ROI pooling s作者: Directed 時(shí)間: 2025-3-28 09:31 作者: 陰謀小團(tuán)體 時(shí)間: 2025-3-28 12:06 作者: saphenous-vein 時(shí)間: 2025-3-28 16:29 作者: 吹牛大王 時(shí)間: 2025-3-28 21:56
Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer His adversarial term. The adversarial term adopts a generator called Residual Attention U-Net (., RAU-Net) to produce the probability maps that cannot be distinguished by the ground-truth. Based on the adversarial model, we can simultaneously estimate the probabilities of many pixels with high-order co作者: 猛擊 時(shí)間: 2025-3-29 01:16
Xuhua Ren,Lichi Zhang,Dongming Wei,Dinggang Shen,Qian Wang作者: 大門在匯總 時(shí)間: 2025-3-29 03:56
Bolin Lai,Shiqi Peng,Guangyu Yao,Ya Zhang,Xiaoyun Zhang,Yanfeng Wang,Hui Zhao作者: Instantaneous 時(shí)間: 2025-3-29 09:19 作者: MUTE 時(shí)間: 2025-3-29 13:03 作者: 包庇 時(shí)間: 2025-3-29 15:33 作者: confederacy 時(shí)間: 2025-3-29 20:18
Sen Zhang,Changzheng Zhang,Lanjun Wang,Cixing Li,Dandan Tu,Rui Luo,Guojun Qi,Jiebo Luo作者: neologism 時(shí)間: 2025-3-30 03:10
Changzheng Zhang,Dong Liu,Lanjun Wang,Yaoxin Li,Xiaoshi Chen,Rui Luo,Shuanlong Che,Hehua Liang,Yingh作者: Vo2-Max 時(shí)間: 2025-3-30 04:27
Feng Yang,Hang Yu,Kamolrat Silamut,Richard J. Maude,Stefan Jaeger,Sameer Antani作者: BRAWL 時(shí)間: 2025-3-30 11:49
Shengyang Li,Xiaoyun Zhang,Xiaoxia Wang,Yumin Zhong,Xiaofen Yao,Ya Zhang,Yanfeng Wang作者: harmony 時(shí)間: 2025-3-30 14:15 作者: 不能妥協(xié) 時(shí)間: 2025-3-30 17:14
Shunbo Hu,Lintao Zhang,Guoqiang Li,Mingtao Liu,Deqian Fu,Wenyin Zhang作者: 2否定 時(shí)間: 2025-3-31 00:03 作者: 群島 時(shí)間: 2025-3-31 04:17 作者: 協(xié)議 時(shí)間: 2025-3-31 05:14 作者: allude 時(shí)間: 2025-3-31 12:39 作者: forecast 時(shí)間: 2025-3-31 14:10 作者: Optimum 時(shí)間: 2025-3-31 21:20
https://doi.org/10.1007/978-3-030-32692-0artificial intelligence; automatic segmentations; ct image; image analysis; image reconstruction; image r作者: mydriatic 時(shí)間: 2025-4-1 00:36
978-3-030-32691-3Springer Nature Switzerland AG 2019作者: rectum 時(shí)間: 2025-4-1 04:02
Machine Learning in Medical Imaging978-3-030-32692-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 碎石 時(shí)間: 2025-4-1 07:25 作者: Genteel 時(shí)間: 2025-4-1 14:02 作者: 兇兆 時(shí)間: 2025-4-1 15:27 作者: 平淡而無味 時(shí)間: 2025-4-1 18:40 作者: tattle 時(shí)間: 2025-4-2 00:15
erden. Wenn Sie heute aufmerksam Ihre Um- und Arbeitswelt betrachten, werden Sie feststellen, da? der Computer mit seinen F?higkeiten, Informationen jeglicher Art zu verarbeiten und wie- derzugeben, fast in jedem Bereich anzutreffen ist. Die digitale Informations- verarbeitung beherrscht unseren All作者: 不知疲倦 時(shí)間: 2025-4-2 03:51 作者: ASSET 時(shí)間: 2025-4-2 10:50
