標(biāo)題: Titlebook: Applications of Medical Artificial Intelligence; First International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2022 T [打印本頁(yè)] 作者: 難免 時(shí)間: 2025-3-21 17:35
書目名稱Applications of Medical Artificial Intelligence影響因子(影響力)
書目名稱Applications of Medical Artificial Intelligence影響因子(影響力)學(xué)科排名
書目名稱Applications of Medical Artificial Intelligence網(wǎng)絡(luò)公開(kāi)度
書目名稱Applications of Medical Artificial Intelligence網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Applications of Medical Artificial Intelligence被引頻次
書目名稱Applications of Medical Artificial Intelligence被引頻次學(xué)科排名
書目名稱Applications of Medical Artificial Intelligence年度引用
書目名稱Applications of Medical Artificial Intelligence年度引用學(xué)科排名
書目名稱Applications of Medical Artificial Intelligence讀者反饋
書目名稱Applications of Medical Artificial Intelligence讀者反饋學(xué)科排名
作者: 殺菌劑 時(shí)間: 2025-3-21 22:28 作者: 現(xiàn)暈光 時(shí)間: 2025-3-22 02:48 作者: Leaven 時(shí)間: 2025-3-22 06:51 作者: negligence 時(shí)間: 2025-3-22 09:47
,ECG-ATK-GAN: Robustness Against Adversarial Attacks on?ECGs Using Conditional Generative Adversariaunction for adversarial perturbation identification and new blocks for discerning and combining out-of-distribution shifts in signals in the learning process for accurately classifying various arrhythmia types. Furthermore, we benchmark our architecture on six different white and black-box attacks a作者: vitreous-humor 時(shí)間: 2025-3-22 16:03
A Deep Learning-Based Interactive Medical Image Segmentation Framework, We propose to introduce a virtual user in the training process, modelled by simulating the user feedback from the current segmentation. We demonstrate our framework on the task of female pelvis MRI segmentation, using a new dataset. We evaluate our framework against existing work with the standard 作者: CURL 時(shí)間: 2025-3-22 20:32
,Deep Neural Network Pruning for?Nuclei Instance Segmentation in?Hematoxylin and?Eosin-Stained Histo layer-wise pruning delivers slightly better performance than network-wide pruning for small compression ratios (CRs) while for large CRs, network-wide pruning yields superior performance. For semantic segmentation, deep regression and final instance segmentation, 93.75%, 95%, and 80% of the model w作者: 粉筆 時(shí)間: 2025-3-22 21:54
Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Seg DCE-MR images obtained from public dataset. The segmentation results clearly show that the proposed network model provides the most accurate delineation results of the breast cancers in the DCE-MR images. The proposed model can be applied to other clinical practice sensitive to spatial information 作者: 華而不實(shí) 時(shí)間: 2025-3-23 02:22 作者: collagen 時(shí)間: 2025-3-23 07:38 作者: figure 時(shí)間: 2025-3-23 12:56
,Wavelet Guided 3D Deep Model to?Improve Dental Microfracture Detection,ikely fractured regions. Based on this fracture probability map we detect the presence of fracture and are able to differentiate a fractured tooth from a control tooth. We compare these results to a 2D CNN-based approach and we show that our approach provides superior detection results. We also show作者: 出血 時(shí)間: 2025-3-23 14:47 作者: 高原 時(shí)間: 2025-3-23 20:41
Seasonal Anoestrus in Wild Sowsith over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcome作者: 拱墻 時(shí)間: 2025-3-24 00:04 作者: 喃喃而言 時(shí)間: 2025-3-24 06:05 作者: cardiopulmonary 時(shí)間: 2025-3-24 09:03 作者: 蜿蜒而流 時(shí)間: 2025-3-24 11:26
Michel Chonchol,Jessica Kendrick We propose to introduce a virtual user in the training process, modelled by simulating the user feedback from the current segmentation. We demonstrate our framework on the task of female pelvis MRI segmentation, using a new dataset. We evaluate our framework against existing work with the standard 作者: 孤僻 時(shí)間: 2025-3-24 17:58
Calcium Homeostasis in Kidney Disease layer-wise pruning delivers slightly better performance than network-wide pruning for small compression ratios (CRs) while for large CRs, network-wide pruning yields superior performance. For semantic segmentation, deep regression and final instance segmentation, 93.75%, 95%, and 80% of the model w作者: expound 時(shí)間: 2025-3-24 20:14
Joshua J. Neumiller,Irl B. Hirsch DCE-MR images obtained from public dataset. The segmentation results clearly show that the proposed network model provides the most accurate delineation results of the breast cancers in the DCE-MR images. The proposed model can be applied to other clinical practice sensitive to spatial information 作者: sphincter 時(shí)間: 2025-3-25 00:34 作者: Exterior 時(shí)間: 2025-3-25 05:19 作者: CARK 時(shí)間: 2025-3-25 08:01
M.-H. Goni,V. Markussis,G. Tolisikely fractured regions. Based on this fracture probability map we detect the presence of fracture and are able to differentiate a fractured tooth from a control tooth. We compare these results to a 2D CNN-based approach and we show that our approach provides superior detection results. We also show作者: climax 時(shí)間: 2025-3-25 12:14 作者: Commodious 時(shí)間: 2025-3-25 18:45
,Deep Learning Meets Computational Fluid Dynamics to?Assess CAD in?CCTA,ed invasive examinations to assess this condition, the current research focus is put on non-invasive procedures. Here, the coronary computed tomography angiography is the first-choice modality, but its manual analysis is cost-inefficient, lacks reproducibility, and suffers from significant inter- an作者: FADE 時(shí)間: 2025-3-25 21:55 作者: instill 時(shí)間: 2025-3-26 04:13 作者: Blanch 時(shí)間: 2025-3-26 06:28
Automated Assessment of Renal Calculi in Serial Computed Tomography Scans,his retrospective study included 722 scans from 330 patients chosen from 8544 asymptomatic patients who underwent two or more CTC (CT colonography) or non-enhanced abdominal CT scans between 2004 and 2016 at a single medical center. A pre-trained deep learning (DL) model was used to segment the kidn作者: 驚奇 時(shí)間: 2025-3-26 09:24
,Prediction of?Mandibular ORN Incidence from?3D Radiation Dose Distribution Maps Using Deep Learningr (HNC) patients treated with radiotherapy (RT). The prediction of mandibular ORN may not only guide the RT treatment planning optimisation process but also identify which patients would benefit from a closer follow-up post-RT for an early diagnosis and intervention of ORN. Existing mandibular ORN p作者: inclusive 時(shí)間: 2025-3-26 12:37
,Analysis of?Potential Biases on?Mammography Datasets for?Deep Learning Model Development,ge. This paper provides an overview of the potential biases that appear in image analysis datasets that affect the development and performance of artificial intelligence algorithms. Especially, an exhaustive analysis of mammography data has been carried out at the patient, image and source of origin作者: concentrate 時(shí)間: 2025-3-26 17:04
,ECG-ATK-GAN: Robustness Against Adversarial Attacks on?ECGs Using Conditional Generative Adversariaearning approaches have reached human-level performance in classifying arrhythmia from ECGs. However, these architectures are vulnerable to adversarial attacks, which can misclassify ECG signals by decreasing the model’s accuracy. Adversarial attacks are small crafted perturbations injected in the o作者: expound 時(shí)間: 2025-3-26 23:53 作者: 含糊 時(shí)間: 2025-3-27 04:47 作者: 控制 時(shí)間: 2025-3-27 08:32 作者: prosperity 時(shí)間: 2025-3-27 12:13
,Deep Neural Network Pruning for?Nuclei Instance Segmentation in?Hematoxylin and?Eosin-Stained Histoand increasing inference speed on specialized hardwares. Although pruning was mainly tested on computer vision tasks, its application in the context of medical image analysis has hardly been explored. This work investigates the impact of well-known pruning techniques, namely layer-wise and network-w作者: 使入迷 時(shí)間: 2025-3-27 14:42
Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segreatment planning. Deep learning has tremendously improved the performances of automated segmentation in a data-driven manner as compared with conventional machine learning models. In this work, we propose a spatial feature conservative design for feature extraction in deep neural networks. To avoid作者: 大氣層 時(shí)間: 2025-3-27 18:30
,The Impact of?Using Voxel-Level Segmentation Metrics on?Evaluating Multifocal Prostate Cancer Locald, when reported alone, for their unclear or even misleading clinical interpretation. DSCs may also differ substantially from HDs, due to boundary smoothness or multiple regions of interest (ROIs) within a subject. More importantly, either metric can also have a nonlinear, non-monotonic relationship作者: 故意 時(shí)間: 2025-3-27 23:18
,OOOE: Only-One-Object-Exists Assumption to?Find Very Small Objects in?Chest Radiographs, neural networks could potentially automate. However, many foreign objects like tubes and various anatomical structures are small in comparison to the entire chest X-ray, which leads to severely unbalanced data and makes training deep neural networks difficult. In this paper, we present a simple yet作者: Irksome 時(shí)間: 2025-3-28 04:39
,Wavelet Guided 3D Deep Model to?Improve Dental Microfracture Detection, crack will continue to progress, often with significant pain, until the tooth is lost. Previous attempts to utilize cone beam computed tomography (CBCT) for detecting cracks in teeth had very limited success. We propose a model that detects cracked teeth in high resolution (hr) CBCT scans by combin作者: BANAL 時(shí)間: 2025-3-28 08:27
,Analysis of?Potential Biases on?Mammography Datasets for?Deep Learning Model Development, levels. Furthermore, we summarize some techniques to alleviate these biases for the development of fair deep learning models. We present a learning task to classify negative and positive screening mammographies and analyze the influence of biases in the performance of the algorithm.作者: wall-stress 時(shí)間: 2025-3-28 10:45 作者: 內(nèi)疚 時(shí)間: 2025-3-28 15:30 作者: BURSA 時(shí)間: 2025-3-28 19:19 作者: arthroscopy 時(shí)間: 2025-3-28 23:04 作者: 遠(yuǎn)足 時(shí)間: 2025-3-29 04:17
Seasonal Anoestrus in Wild Sows using both simulated and clinical study data in a multitask regression setting. The results not only show promising performance in detecting near and far out-of-distribution data cases, but may also suggest the improved performance in predicting GA growth rate for in-distribution data.作者: beta-cells 時(shí)間: 2025-3-29 09:37
,Deep Learning Meets Computational Fluid Dynamics to?Assess CAD in?CCTA,cquired scans, revealed that the suggested segmentation approaches not only outperform state-of-the-art nnU-Nets, but also lead to the blood-flow parameters which are in strong agreement with those elaborated for the ground-truth delineations.作者: 縱火 時(shí)間: 2025-3-29 13:43
Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence, using both simulated and clinical study data in a multitask regression setting. The results not only show promising performance in detecting near and far out-of-distribution data cases, but may also suggest the improved performance in predicting GA growth rate for in-distribution data.作者: faddish 時(shí)間: 2025-3-29 17:16
Oscar Escobar,Eliana M. Perez-Garcia levels. Furthermore, we summarize some techniques to alleviate these biases for the development of fair deep learning models. We present a learning task to classify negative and positive screening mammographies and analyze the influence of biases in the performance of the algorithm.作者: 喧鬧 時(shí)間: 2025-3-29 23:41 作者: Culpable 時(shí)間: 2025-3-30 01:44 作者: grieve 時(shí)間: 2025-3-30 05:57 作者: Jejune 時(shí)間: 2025-3-30 10:18
,Was that?so?Hard? Estimating Human Classification Difficulty, truth human difficulty for each image case in a dataset using self-assessed certainty. We apply our methods to two different medical datasets, achieving high Kendall rank correlation coefficients on both, showing that we outperform existing methods by a large margin on our problem and data.作者: acrobat 時(shí)間: 2025-3-30 16:14
Conference proceedings 2022 in conjunction with MICCAI 2022, in Singapore, in September 2022.?.The book includes 17 papers which were carefully reviewed and selected from 26 full-length submissions..Practical applications of medical AI bring in new challenges and opportunities.The AMAI workshop aims to engage medical AI pract作者: CLAM 時(shí)間: 2025-3-30 20:04
https://doi.org/10.1007/978-3-031-17721-7artificial intelligence; bioinformatics; computer networks; computer systems; computer vision; deep learn
